Polarization information of the light can provide rich cues for computer vision and scene understanding tasks, such as the type of material, pose, and shape of the objects. With the advent of new and cheap polarimetric sensors, this imaging modality is becoming accessible to a wider public for solving problems, such as pose estimation, 3D reconstruction, underwater navigation, and depth estimation. However, we observe several limitations regarding the usage of this sensorial modality, as well as a lack of standards and publicly available tools to analyze polarization images. Furthermore, although polarization camera manufacturers usually provide acquisition tools to interface with their cameras, they rarely include processing algorithms that make use of the polarization information. In this work, we review recent advances in applications that involve polarization imaging, including a comprehensive survey of recent advances on polarization for vision and robotics perception tasks. We also introduce a complete software toolkit that provides common standards to communicate with and process information from most of the existing micro-grid polarization cameras on the market. The toolkit also implements several image processing algorithms for this modality, and it is publicly available on GitHub.
Malignant melanoma is the most dangerous type of skin cancer, yet it is the most treatable kind of cancer, conditioned by its early diagnosis which is a challenging task for clinicians and dermatologists. In this regard, CAD systems based on machine learning and image processing techniques are developed to differentiate melanoma lesions from benign and dysplastic nevi using dermoscopic images. Generally, these frameworks are composed of sequential processes: pre-processing, segmentation, and classification. This architecture faces mainly two challenges: (i) each process is complex with the need to tune a set of parameters, and is specific to a given dataset; (ii) the performance of each process depends on the previous one, and the errors are accumulated throughout the framework. In this paper, we propose a framework for melanoma classification based on sparse coding which does not rely on any pre-processing or lesion segmentation. Our framework uses Random Forests classifier and sparse representation of three features: SIFT, Hue and Opponent angle histograms, and RGB intensities. The experiments are carried out on the public PH2 dataset using a 10-fold cross-validation. The results show that SIFT sparse-coded feature achieves the highest performance with sensitivity and specificity of 100% and 90.3% respectively, with a dictionary size of 800 atoms and a sparsity level of 2. Furthermore, the descriptor based on RGB intensities achieves similar results with sensitivity and specificity of 100% and 71.3%, respectively for a smaller dictionary size of 100 atoms. In conclusion, dictionary learning techniques encode strong structures of dermoscopic images and provide discriminant descriptors.
Melanoma is the deadliest type of skin cancer, yet it is the most treatable kind depending on its early diagnosis. The early prognosis of melanoma is a challenging task for both clinicians and dermatologists. Due to the importance of early diagnosis and in order to assist the dermatologists, we propose an automated framework based on ensemble learning methods and dermoscopy images to differentiate melanoma from dysplastic and benign lesions. The evaluation of our framework on the recent and public dermoscopy benchmark (PH2 dataset) indicates the potential of proposed method. Our evaluation, using only global features, revealed that ensembles such as random forest perform better than single learner. Using random forest ensemble and combination of color and texture features, our framework achieved the highest sensitivity of 94% and specificity of 92%.
A nonparametric method to define a pixel neighborhood in catadioptric images is presented. The method is based on an accurate modeling of the mirror shape by mean of polarization imaging. Unlike most processing methods existing in the literature, this method is nonparametric and enables us to respect the catadioptric image’s anamorphosis. The neighborhood is directly derived from the two polarization parameters: the angle and the degree of polarization. Regardless of the shape of the catadioptric sensor’s mirror (including noncentral configurations), image processing techniques such as image derivation, edge detection, interest point detection, as well as image matching, can be efficiently performed.
We present a new efficient method for calibration of catadioptric sensors. The method is based on an accurate measurement of the three-dimensional parameters of the mirror through polarization imaging. While inserting a rotating polarizer between the camera and the mirror, the system is automatically calibrated without any calibration patterns. Moreover, this method permits most of the constraints related to the calibration of catadioptric systems to be relaxed. We show that, contrary to our system, the traditional methods of calibration are very sensitive to misalignment of the camera axis and the symmetry axis of the mirror. From the measurement of three-dimensional parameters, we apply the generic calibration concept to calibrate the catadioptric sensor. We also show the influence of the disturbed measurement of the parameters on the reconstruction of a synthetic scene. Finally, experiments prove the validity of the method with some preliminary results on three-dimensional reconstruction.
This paper aims at reviewing the recent published works dealing with industrial applications which rely on polarization imaging.
A general introduction presents the basics of polarimetry and then 2D and 3D machine vision application are presented as well as the latest evolution in term of high speed polarimetric imaging.
In order to achieve better quality on their products, manufacturers
now use more and more artificial vision systems during their process.
Concerning transparent objects the task is not trivial and requires controlling the whole lighting of the scene. This paper deals with a polarization imaging method and its application to shape measurement of transparent objects. Our aim is to develop a low cost system based on a unique viewpoint and using industrial components. We show how to overcome ambiguities appearing during the measurement process.
A new efficient method of calibration for catadioptric sensors is presented in this paper. It is based on an accurate measurement of the three-dimensional parameters of the mirror by means of polarization imaging. While inserting a rotating polarizer between the camera and the mirror, the system is automatically calibrated without and calibration patterns. Moreover it permits to relax most of the constraints related to the calibration of the catadioptric systems. We show that contrary to our system, the traditional methods of calibration are very sensitive to misalignment of the camera axis and the symmetry axis of the mirror. From the measurement of three-dimensional parameters, we apply the generic calibration concept to calibrate the catadioptric sensor. The influence of the disturbed measurement of the parameters on the reconstruction of a synthetic scene is also presented. Finally, experiments prove the validity of the method with some preliminary results on three-dimensional reconstruction.
Specular surfaces inspection remains a delicate task within the automatic control of products made by plastic plating. These objects are of very varied shape and their surface is highly reflective acting like a mirror. This paper presents steps to follow in order to detect geometric aspect surface defects on objects made by plastic plating. The projection of a binary fringes pattern is used and enables to reveal the defects near the transition between a dark fringe and a bright fringe. Indeed, the surface imperfections provoke important light rays deviations. By moving this dynamic lighting, and thanks to a saturated camera, the system brings an aspect image where the defects appear very contrasted on a dark background. A simple image processing algorithm is then applied leading to a very efficient segmentation. To obtain such resulting images, the translation step, the duty cycle and also the number of images are constraint. This article finally shows how to adjust these parameters according to the various sizes of defect and to the objects shape.
We propose a new application of « Shape from Polarization » method to reconstruct surface shapes of specular metallic objects. Studying the polarization state of the reflected light is very useful to get information on the surface normals. After reflection, an unpolarized light wave becomes partially linearly polarized. Such a wave, can be described by its three parameters: intensity, degree of polarization, and angle of polarization. By using the refractive index of the surface, a relationship between the degree of polarization and the reflection angle can be established. Unfortunately, the relation commonly used for dielectrics, cannot be applied since the refractive index of metallic surfaces is complex. To get a similar relation, we apply a usual approximation valid in the visible region. The Fresnel reflectance model can also provide a relationship between the angle of polarization and the incidence plane orientation. Thus, the reflection angle and the incidence plane orientation give the surface normals. The shape is finally computed by integrating the normals with a relaxation algorithm.
Applications on metallic objects made by stamping and polishing are also described, and show the efficiency of our system to discriminate shape defects. Future works will consist in integrating the system into an automatic process of defects detection.
In the field of industrial vision, extracting the 3D shape information of highly reflective metallic objects is still a delicate task. This paper presents a new application of "Shape from Polarization" method to specular metallic surfaces. Studying the state of polarization of the reflected light is very useful to get information on the normals of the surface. This article demonstrates how to extend the commonly used method for dielectric to metallic surfaces. Applications on shape defects detection are also discussed, and the efficiency of the system to discriminate defects on metallic objects made by stamping and polishing is presented.
This paper deals with the analysis of ancient wooden stamps. The aim is to extract a binary image from the stamp. This image must be the closer to the image produced by inking and using a printing press with the stamps. A range image based method is proposed to extract a stamped image from the stamps. The range image acquisition from a 3D laser scanner is presented. Pre-filtering for range image enhancement is detailed. The range image binarization method is based on an adaptive thresholding. Few simple processes applied on the range image enable a final binarized image computing. The proposed method provides here a very efficient way to perform "virtual" stampings with ancient wooden stamps.
KEYWORDS: 3D scanning, Biological research, 3D image processing, Scanners, Image analysis, 3D modeling, 3D acquisition, Image filtering, Wavelets, Image acquisition
We propose in this paper an application of multiresolution analysis techniques to extract information contained in the growth increments of a bivalve mollusk called: Calyptogena. The first stage consists in extracting a range image of the mollusk’s shell using a 3-D scanner. Applying a multiresolution analysis enables us to localize precisely those growth increments by preserving relevant details. Moreover, interesting spatial and frequency properties of the multiresolution analysis underline information contained on the shell. Intra-individual variation and inter-individual variations are compared to assume some conclusions as for the ontogenetic evolution of the animal such as periodicities, which can be later related to certain regular changes in its environment.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.