Detecting salient cues through illumination-invariant color ratios

Todt, Eduardo; Torras, Carme

doi:10.1016/j.robot.2004.06.003

Cited by 10 publications

(12 citation statements)

References 27 publications

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“…c 1 c 2 c 3 was proposed to achieve independency of color illumination and discount the object's geometry, l 1 l 2 l 3 was proposed to determine the direction of the triangular color plane in RGB space and m 1 m 2 m 3 achieved a constant color model considering a change in spectral power distribution of the illumination [33,34,38]. Nevertheless, it should be noted that the former color spaces were built assuming dichromatic reflectance and white illumination.…”

Section: Image Preprocessing Challenges Color Spacesmentioning

confidence: 99%

Improved Skin Lesion Diagnostics for General Practice by Computer-Aided Diagnostics

Rosado¹,

Vasconcelos²,

Castro³

et al. 2015

Dermoscopy Image Analysis

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Section: Image Preprocessing Challenges Color Spacesmentioning

confidence: 99%

Improved Skin Lesion Diagnostics for General Practice by Computer-Aided Diagnostics

Rosado¹,

Vasconcelos²,

Castro³

et al. 2015

Dermoscopy Image Analysis

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“…The visual saliency of elements in the images is detected with the color-ratios saliency algorithm [37], which has the interesting characteristic of embedding color constancy within the saliency computation.…”

Section: Looking For Landmarks Based On Visual Saliencymentioning

confidence: 99%

“…Among these are edge or line orientation, color, motion, and stereo. One major observation is that the relevant variable is not the amplitude of visual signals in a particular feature dimension, but the contrast between this amplitude at a given point and at the corresponding surroundingThe visual saliency of elements in the images is detected with the color-ratios saliency algorithm [37], which has the interesting characteristic of embedding color constancy within the saliency computation.The color constancy counterbalances the intrinsic variations of illumination outdoors that can affect the color perception and, subsequently, the saliency results. In the following, this algorithm is described shortly.…”

mentioning

confidence: 99%

Outdoor View Recognition Based on Landmark Grouping and Logistic Regression

Todt

Torras

2013

Int. J. Patt. Recogn. Artif. Intell.

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Vision-based robot localization outdoors has remained more elusive than its indoors counterpart. Drastic illumination changes and the scarceness of suitable landmarks are the main difficulties. This paper attempts to surmount them by deviating from the main trend of using local features. Instead, a global descriptor called landmark-view is defined, which aggregates the most visually-salient landmarks present in each scene. Thus, landmark co-occurrence and spatial and saliency relationships between them are added to the single landmark characterization, based on saliency and color distribution. A suitable framework to compare landmark-views is developed, and it is shown how this remarkably enhances the recognition performance, compared against single landmark recognition. A view-matching model is constructed using logistic regression. Experimentation using 45 views, acquired outdoors, containing 273 landmarks, yielded good recognition results. The overall percentage of correct view classification obtained was 80.6%, indicating the adequacy of the approach.Keywords: visual landmarks, visual saliency, robot navigation, autonomous robot IntroductionThe extraction of reliable visual landmarks for mobile robot localization in unknown outdoor unstructured environments is still an open research problem. One of the key factors that makes the detection and recognition of visual landmarks in outdoor environments, as well as indoors without dominant artificial illumination, a challenging task is that acquired visual information is strongly dependent on lighting geometry (direction and intensity of light source) and illuminant color (spectral power distribution), which change with sun position and atmospheric conditions.Most feature extraction approaches are not adequate for this type of environments, since they rely on either structured information from non-deformable objects [7], or on a priori knowledge about the landmarks [3]. Several recent works achieved interesting results using SIFT features to match pairs of images [18,22,32], which can be extended to the landmark recognition problem. Since mobile robot navigation tasks require real-time execution, some efforts have been made to reduce the considerable computational cost necessary to evaluate SIFT features for a whole image [17,28]. Also it was reported that SIFT features fail to consider global context to resolve ambiguities that can occur locally in images, motivating solutions that improve the amount of global information used in the descriptors [10,24] A suitable framework to compare views is developed, and it is shown how this remarkably enhances the recognition performance.The remainder of the paper is organized as follows. Section 2 presents the concept of landmark and visual saliency. In this context, landmarks are the visual elements that are used to provide the features for the recognition of places, and they are found or defined based on classical visual saliency criteria, inspired in a biological model of visual opponency. Section 3 describes how to ref...

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“…Therefore, some robot methods (Lehrer and Bianco 2000) resort to a strategy observed in bees and wasps: when departing from a novel goal location, these insects perform stereotypic flight manoeuvres called 'turn-back-and-look' behaviour -during these movement sequences, robust landmarks could be selected by a tracking process. Specifically for outdoor navigation, invariance against changes in illumination becomes an important property; colour opponency mechanisms have been suggested as a possible solution, from both the technical and the biological perspective (Möller 2002b, Todt andTorras 2004). However, a small number of guidance methods deviate from the concept of landmark extraction and matching.…”

Section: Visual Homing Strategies For Mobile Robotsmentioning

confidence: 99%

Biologically plausible visual homing methods based on optical flow techniques

2005

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Insects are able to return to important places in their environment by storing an image of the surroundings while at the goal, and later computing a home direction from a matching between this 'snapshot' image and the currently perceived image. Very similar ideas are pursued for the visual navigation of mobile robots. A wide range of different solutions for the matching between the two images have been suggested. This paper explores the application of optical flow techniques for visual homing. The performance of five different flow techniques and a reference method is analysed based on image collections from three different indoor environments. We show that block matching, two simple variants of block matching and two even simpler differential techniques produce robust homing behaviour, despite the simplicity of the matched features. Our analysis reveals that visual homing can succeed even in the presence of many incorrect feature correspondences, and that low-frequency features are sufficient for homing. In particular, the successful application of differential methods opens new vistas on the visual homing problem, both as plausible and parsimonious models of visual insect navigation, and as a starting point for novel robot navigation methods.

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Detecting salient cues through illumination-invariant color ratios

Cited by 10 publications

References 27 publications

Improved Skin Lesion Diagnostics for General Practice by Computer-Aided Diagnostics

Improved Skin Lesion Diagnostics for General Practice by Computer-Aided Diagnostics

Outdoor View Recognition Based on Landmark Grouping and Logistic Regression

Biologically plausible visual homing methods based on optical flow techniques

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