Energy-Based Segmentation of Very Sparse Range Surfaces

This paper describes a new segmentation technique for very sparse surfaces which is based on minimizing the energy of the surfaces in the scene. While it could be used in almost any system as part of surface reconstruction/model recovery, the algorithm is designed to be usable when the depth information is scattered and very sparse, as is generally the case with depth generated by stereo algorithms. We show results from a sequential algorithm that processes laser range-finder data or synthetic data. We then discuss a parallel implementation running on the parallel Connection Machine. The idea of segmentation by energy minimization is not new. However, prior techniques have relied on discrete regularization or Markov random fields to model the surfaces to build smooth surfaces and detect depth edges. Both of the aforementioned techniques are ineffective at energy minimization for very sparse data. In addition, this method does not require edge detection and is thus also applicable when edge information is unreliable or unavailable. Our model is extremely general; it does not depend on a model of the surface shape but only on the energy for bending a surface. Thus the surfaces can grow in a more data-directed manner. The technique presented herein models the surfaces with reproducing kernel-based splines, which can be shown to solve a regularized surface reconstruction problem. From the functional form of these splines we derive computable bounds on the energy of a surface over a given finite region. The computation of the spline, and the corresponding surface representation are quite efficient for very sparse data. An interesting property of the algorithm is that it makes no attempt to determine segmentation boundaries; the algorithm can be viewed as a classification scheme which segments the data into collections of points which are "from" the same surface. Among the significant advantages of the method is the capacity to process overlapping transparent surfaces, as well as surfaces with large occluded areas

Analysis of the detachment of citrus fruits by vibration using artificial vision

The vibratory behaviour of citrus fruits is studied using slow-motion cameras in order to gain a better understanding of the parameters involved in fruit detachment when mechanical harvesting is done using shakers. Single citrus fruits with a small portion of stem were vibrated using strokes from 60 mm to 180 mm and frequencies from 3 Hz to 18 Hz. The movement was recorded at 300 fps and the main parameters considered for fruit detachment were determined through the analysis of the video sequences. Image-processing algorithms created for this purpose were applied to the automated estimation of the centroid of the fruit, the angle of the stem pistil axis, and the position of some selected points in the fruit in each frame of the video sequences to obtain dynamic parameters such as the position, speed and acceleration of the fruit during the movement until it is detached. The signals obtained from the image processing were filtered, providing results in accordance with the calibration systems. In general, results suggest that the inertial forces transmitted to the fruit were lower than the tensile forces required to detach the fruit by pulling it in the stem pistil direction. The largest peaks were observed using long strokes that required fewer cycles for detachment. On the other hand, short strokes combined with high frequencies needed more cycles, and thus a fatigue phenomenon was present. Short strokes and low frequencies were unable to detach some fruit. (C) 2014 IAgrE. Published by Elsevier Ltd. All rights reserved.This work was founded by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA) through the projects RTA2009-00118-C02-01 and RTA2009-00118-C02-02, and co-founded by European FEDER founds.Torregrosa Mira, A.; Albert Gil, FE.; Aleixos Borrás, MN.; Ortiz Sánchez, MC.; Blasco Ivars, J. (2014). Analysis of the detachment of citrus fruits by vibration using artificial vision. Biosystems Engineering. 119:1-12. https://doi.org/10.1016/j.biosystemseng.2013.12.010S11211

Omnidirectional vision on UAV for attitude computation

International audienceUnmanned Aerial Vehicles (UAVs) are the subject of an increasing interest in many applications. Autonomy is one of the major advantages of these vehicles. It is then necessary to develop particular sensors in order to provide efficient navigation functions. In this paper, we propose a method for attitude computation catadioptric images. We first demonstrate the advantages of the catadioptric vision sensor for this application. In fact, the geometric properties of the sensor permit to compute easily the roll and pitch angles. The method consists in separating the sky from the earth in order to detect the horizon. We propose an adaptation of the Markov Random Fields for catadioptric images for this segmentation. The second step consists in estimating the parameters of the horizon line thanks to a robust estimation algorithm. We also present the angle estimation algorithm and finally, we show experimental results on synthetic and real images captured from an airplane

Spatial fuzzy c-mean sobel algorithm with grey wolf optimizer for MRI brain image segmentation

Segmentation is the process of dividing the original image into multiple sub regions called segments in such a way that there is no intersection between any two regions. In medical images, the segmentation is hard to obtain due to the intensity similarity among various regions and the presence of noise in medical images. One of the most popular segmentation algorithms is Spatial Fuzzy C-means (SFCM). Although this algorithm has a good performance in medical images, it suffers from two issues. The first problem is lack of a proper strategy for point initialization step, which must be performed either randomly or manually by human. The second problem of SFCM is having inaccurate segmented edges. The goal of this research is to propose a robust medical image segmentation algorithm that overcomes these weaknesses of SFCM for segmenting magnetic resonance imaging (MRI) brain images with less human intervention. First, in order to find the optimum initial points, a histogram based algorithm in conjunction with Grey Wolf Optimizer (H-GWO) is proposed. The proposed H-GWO algorithm finds the approximate initial point values by the proposed histogram based method and then by taking advantage of GWO, which is a soft computing method, the optimum initial values are found. Second, in order to enhance SFCM segmentation process and achieve higher accurate segmented edges, an edge detection algorithm called Sobel was utilized. Therefore, the proposed hybrid SFCM-Sobel algorithm first finds the edges of the original image by Sobel edge detector algorithm and finally extends the edges of SFCM segmented images to the edges that are detected by Sobel. In order to have a robust segmentation algorithm with less human intervention, the H-GWO and SFCM-Sobel segmentation algorithms are integrated to have a semi-automatic robust segmentation algorithm. The results of the proposed H-GWO algorithms show that optimum initial points are achieved and the segmented images of the SFCM-Sobel algorithm have more accurate edges as compared to recent algorithms. Overall, quantitative analysis indicates that better segmentation accuracy is obtained. Therefore, this algorithm can be utilized to capture more accurate segmented in images in the era of medical imaging