Robust Color Contour Object Detection Invariant to Shadows

Scandaliaris, J.; Villamizar, Michael; Andrade‐Cetto, Juan; Sanfeliu, Alberto

doi:10.1007/978-3-540-76725-1_32

Cited by 4 publications

(9 citation statements)

References 7 publications

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“…The authors concluded that for detecting and discriminating all color groups, there is no single optimal color space. Scandaliaris et al (2007) reported the color and contour-based object detection. They used RGB, C1C2C3, and o1o2 color space images to show the robustness of their models in changing the illumination and varying shadows.…”

Section: Related Workmentioning

confidence: 99%

Comparing shape descriptor methods for different color space and lighting conditions

Mukhopadhyay¹,

Mukherjee

Biswas³

2019

AIEDAM

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Detecting and recognizing objects is one of the most important uses of vision systems in nature and is consequently highly evolved. This paper aims to accurately detect an object using its shape and color information from a complex background. In particular, we evaluated our algorithm to detect 19 different integrated circuits (IC) from 10 different printed circuit boards (PCB) of different colors. We have compared three different shape descriptors for four different color space models. We have evaluated shape detection algorithms in different lighting conditions (indoor, outdoor, and controlled light source) to find suitable illumination for image acquisition. We undertook statistical hypothesis testing to find the effect of color space models and shape descriptors on the accuracy, false positive and false negative rates. While measuring accuracy, we have noted that L*a*b* color space is significantly worse, and the best result is obtained in YCbCr color space using bounding box shape descriptors for 2500 Lux using LED.

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Section: Related Workmentioning

confidence: 99%

Comparing shape descriptor methods for different color space and lighting conditions

Mukhopadhyay¹,

Mukherjee

Biswas³

2019

AIEDAM

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“…The invariant gradient detector that we propose is based on the work of Gevers [8] and the modifications proposed in [7]. This detector uses three color models that have different and complementary properties regarding their response: RGB, c1c2c3 and o1o2.…”

Section: A Color Modelsmentioning

confidence: 99%

“…One way to eliminate these maxima is propagating the uncertainties associated to the color models as well as the different gradient moduli. In order to do this, we calculate the gradient magnitude of the RGB, c1c2c3 and o1o2 color models, and then, we propagate (see [7] for details) the RGB uncertainties, assumed to be known, through the various color models up to the gradient magnitudes, using the uncertainties associated with the gradient magnitude of each color space, σ ∇ C . Finally we define the gradient product…”

Section: B Color Invariant Gradientmentioning

confidence: 99%

“…In the same way, SIFT features [6] compute fixed HoG descriptors in a grid of 4 × 4 cells and 8 gradient orientations around key points. In Scandaliaris et al [7], we proposed to use color based invariant contours and compared them against simple intensity contours in object detection domain under shadows. The proposed method outperformed classical methods and showed that invariant contours can be extracted.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we use pyramidal Histograms of Oriented Gradients in order to have more discriminative and robust descriptors with which to describe objects or scenes, and to face up the drawback of cast shadows using color based invariant gradients (referred to as invariant gradients from now on) [7] that reduce illumination and shadow effects and improve detection results in robotics applications. The descriptors extracted during the learning phase (see section IV) encode relevant and invariant features useful for detection.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Combining color-based invariant gradient detector with HoG descriptors for robust image detection in scenes under cast shadows

Villamizar

Scandaliaris

Sanfeliu

et al. 2009

2009 IEEE International Conference on Robotics and Automation

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In this work we present a robust detection method in outdoor scenes under cast shadows using color based invariant gradients in combination with HoG local features. The method achieves good detection rates in urban scene classification and person detection outperforming traditional methods based on intensity gradient detectors which are sensible to illumination variations but not to cast shadows. The method uses color based invariant gradients that emphasize material changes and extract relevant and invariant features for detection while neglecting shadow contours. This method allows to train and detect objects and scenes independently of scene illumination, cast and self shadows. Moreover, it allows to do training in one shot, that is, when the robot visits the scene for the first time.

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Binocular visual tracking and grasping of a moving object with a 3D trajectory

Fuentes-Pacheco¹,

Ruiz-Ascencio²,

Rendón-Mancha³

2009

JART

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This paper presents a binocular eye-to-hand visual servoing system that is able to track and grasp a moving object in real time. Linear predictors are employed to estimate the object trajectory in three dimensions and are capable of predicting future positions even if the object is temporarily occluded. For its development we have used a CRS T475 manipulator robot with six degrees of freedom and two fixed cameras in a stereo pair configuration. The system has a client‐server architecture and is composed of two main parts: the vision system and the control system. The vision system uses color detection to extract the object from the background and a tracking technique based on search windows and object moments. The control system uses the RobWork library to generate the movement instructions and to send them to a C550 controller by means of the serial port. Experimental results are presented to verify the validity and the efficacy of the proposed visual servoing system.

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Robust Color Contour Object Detection Invariant to Shadows

Cited by 4 publications

References 7 publications

Comparing shape descriptor methods for different color space and lighting conditions

Comparing shape descriptor methods for different color space and lighting conditions

Combining color-based invariant gradient detector with HoG descriptors for robust image detection in scenes under cast shadows

Binocular visual tracking and grasping of a moving object with a 3D trajectory

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