9 research outputs found
Multi Cost Function Fuzzy Stereo Matching Algorithm for Object Detection and Robot Motion Control
Stereo matching algorithms work with multiple images of a scene, taken from two viewpoints, to generate depth information. Authors usually use a single matching function to generate similarity between corresponding regions in the images. In the present research, the authors have considered a combination of multiple data costs for disparity generation. Disparity maps generated from stereo images tend to have noisy sections. The presented research work is related to a methodology to refine such disparity maps such that they can be further processed to detect obstacle regions. A novel entropy based selective refinement (ESR) technique is proposed to refine the initial disparity map. The information from both the left disparity and right disparity maps are used for this refinement technique. For every disparity map, block wise entropy is calculated. The average entropy values of the corresponding positions in the disparity maps are compared. If the variation between these entropy values exceeds a threshold, then the corresponding disparity value is replaced with the mean disparity of the block with lower entropy. The results of this refinement are compared with similar methods and was observed to be better. Furthermore, in this research work, the v-disparity values are used to highlight the road surface in the disparity map. The regions belonging to the sky are removed through HSV based segmentation. The remaining regions which are our ROIs, are refined through a u-disparity area-based technique. Based on this, the closest obstacles are detected through the use of k-means segmentation. The segmented regions are further refined through a u-disparity image information-based technique and used as masks to highlight obstacle regions in the disparity maps. This information is used in conjunction with a kalman filter based path planning algorithm to guide a mobile robot from a source location to a destination location while also avoiding any obstacle detected in its path. A stereo camera setup was built and the performance of the algorithm on local real-life images, captured through the cameras, was observed. The evaluation of the proposed methodologies was carried out using real life out door images obtained from KITTI dataset and images with radiometric variations from Middlebury stereo dataset
Blending Learning and Inference in Structured Prediction
In this paper we derive an efficient algorithm to learn the parameters of
structured predictors in general graphical models. This algorithm blends the
learning and inference tasks, which results in a significant speedup over
traditional approaches, such as conditional random fields and structured
support vector machines. For this purpose we utilize the structures of the
predictors to describe a low dimensional structured prediction task which
encourages local consistencies within the different structures while learning
the parameters of the model. Convexity of the learning task provides the means
to enforce the consistencies between the different parts. The
inference-learning blending algorithm that we propose is guaranteed to converge
to the optimum of the low dimensional primal and dual programs. Unlike many of
the existing approaches, the inference-learning blending allows us to learn
efficiently high-order graphical models, over regions of any size, and very
large number of parameters. We demonstrate the effectiveness of our approach,
while presenting state-of-the-art results in stereo estimation, semantic
segmentation, shape reconstruction, and indoor scene understanding
Absolute three-dimensional shape measurement using coded fringe patterns without phase unwrapping or projector calibration
This paper presents a novel stereo-phase-based absolute three-dimensional (3D) shape measurement that requires neither phase unwrapping nor projector calibration. This proposed method can be divided into two steps: (1) obtain a coarse disparity map from the quality map; and (2) refine the disparity map using wrapped phase. Fringe patterns are modified to encode the quality map for efficient and accurate stereo matching. Experiments demonstrated that the proposed method could achieve high-quality 3D measurement even with extremely low-quality fringe patterns.This paper was published in Optics Express and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: http://dx.doi.org/10.1364/OE.22.001287. Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law.</p
Occlusion handling in correlation-based matching
In binocular stereovision, the accuracy of the 3D reconstruction depends on the accuracy of matching results.
Consequently, matching is an important task. Our first goal is to present a state of the art of matching methods. We
define a generic and complete algorithm based on essential components to describe most of the matching methods.
Occlusions are one of the most important difficulties and we also present a state of the art of methods dealing with
occlusions. Finally, we propose matching methods using two correlation measures to take into account occlusions. The
results highlight the best method that merges two disparity maps obtained with two different measures.En stéréovision binoculaire, la mise en correspondance est une étape cruciale pour réaliser la reconstruction
3D de la scène. De très nombreuses publications traitent ce problème. Ainsi, le premier objectif est de
proposer un état de l'art des méthodes de mise en correspondance. Nous synthétisons cette étude en
présentant un algorithme générique complet faisant intervenir des éléments constituants permettant de
décrire les différentes étapes de la recherche de correspondances. Une des plus grandes difficultés, au cours
de l'appariement, provient des occultations. C'est pourquoi le second objectif est de présenter un état de l'art
des méthodes qui prennent en compte cette difficulté. Enfin, le dernier objectif est de présenter de nouvelles
méthodes hybrides, dans le cadre des méthodes locales à base de corrélation. Nous nous appuyons sur
l'utilisation de deux mesures de corrélation permettant de mieux prendre en compte le problème des
occultations. Les résultats mettent en évidence la meilleure méthode qui consiste à fusionner deux cartes de
disparités obtenues avec des mesures différentes
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View synthesis for depth from motion 3D x-ray imaging.
The depth from motion or kinetic depth X-ray imaging (KDEX) technique is designed to enhance the luggage screening at airport checkpoints. The technique requires multiple views of the luggage to be obtained from an arrangement of linear X-ray detector arrays. This research investigated a solution to the unique problems defined when considering the possibility of replacing some of the X-ray sensor views with synthetic images. If sufficiently high quality synthetic images can be generated then intermediary X-ray sensors can be removed to minimise the hardware requirements and improve the commercial viability of the KDEX technique. Existing image synthesis algorithms are developed for visible light images. Due to fundamental differences between visible light and X-ray images, those algorithms are not directly applicable to the X-ray scenario. The conditions imposed by the X-ray images have instigated the original research and novel algorithm development and experimentation that form the body of this work. A voting based dual criteria multiple X-ray images synthesis algorithm (V-DMX) is proposed to exploit the potential of two matching criteria and information contained in a sequence of images. The V-DMX algorithm is divided into four stages