Model-based Machine Vision Research Articles

Identifying immature and mature pomelo fruits in trees by elliptical model fitting in the Cr–Cb color space

In precision agriculture, identification of fruit in trees is important. Furthermore, it is also essential for estimating the yield, targeting the exact location for a harvesting robot and selectively harvesting the fruit. An elliptical boundary model-based machine vision algorithm was developed to identify immature and mature pomelo fruit in trees. In the proposed solution, the images were converted from RGB space to Y′CbCr space. Then, ordinary least-squares (OLS) was introduced in fitting implicit second order polynomials of elliptical boundary models in the Cr–Cb color space for segmenting immature green fruits, mature green, green partial white, green partial yellow and green partial red fruits. Those elliptical boundary models along with area opening mathematical morphology and diameter thresholding were applied in the identification procedure. The algorithm was tested on a set of 200 validation images acquired under natural illumination conditions. The results of the validation test showed that the total correct identification rate was 93.5%. The total false positive, missed rate, repeated rate and merged rate were equal to 8.2, 6.5, 10.2 and 10.6%, respectively. The proposed method performed better in detecting mature fruit, the color of which is different from green, than in detecting immature green fruit. On average, the segmenting time for 640 × 480 and 1280 × 960 images were 0.134 and 0.200 s, respectively and the total identification time for 640 × 480 and 1280 × 960 images were 0.240 and 0.362 s, respectively.

Towards Active Machine-Vision-Based Driver Assistance for Urban Areas

Currently available driver assistance systems (i) warn the driver based on vehicle state sensors (e.g., door open, outside temperature near or below the freezing point), (ii) offer route guidance information (navigation systems based on GPS and digital road maps), or—in some critical situations—(iii) even actively influence vehicle handling under carefully delimited conditions (anti-blocking-system, electronic-stability-program). This contribution reports about investigations to combine passive GPS- and map-based route guidance with model-based machine vision in order to automatically assess or even execute driving maneuvers in inner-city traffic situations. Information provided by todays route guidance systems is treated as a generic description of lane structure. The schematic description of lane structures extractable from commercially available standard digital maps is automatically instantiated by a machine vision approach which interprets video image sequences recorded by cameras from within a driving vehicle. The resulting model of the lane structure in front of the vehicle is subsequently exploited in order to control vehicle maneuvers in real-time as a proof of principal system competences. The machine-vision-based execution of driving maneuvers can at any time be overridden by the driver.

A model-based machine vision system using fuzzy logic

An effective model-based machine vision system is designed for use in practical production lines. In the proposed system, the gray level corner is selected as a local feature, and a gray level corner detector is developed. The gray level corner detection problem is formulated as a pattern classification problem to determine whether a pixel belongs to the class of corners or not. The probability density function is estimated by means of fuzzy logic. A corner matching method is developed to minimize the amount of calculation. All available information obtained from the gray level corner detector is used to make the model. From a fuzzy inference procedure, a matched segment list is extracted, and the resulted segment list is used to calculate the transformations between the model object and each object in the scene. In order to reduce the fuzzy rule set, a notion of overlapping cost is introduced. To show the effectiveness of the developed algorithm, simulations are conducted for synthetic images, and an experiment is conducted on an image of a real industrial component.

Distributions of projective invariants and model-based machine vision

In machine vision, objects are observed subject to an unknown projective transformation, and it is usual to use projective invariants for either testing for a false alarm or for classifying an object. For four collinear points, the cross-ratio is the simplest statistic which is invariant under projective transformations. We obtain the distribution of the cross-ratio under the Gaussian error model with different means. The case of identical means, which has appeared previously in the literature, is derived as a particular case. Various alternative forms of the cross-ratio density are obtained, e.g. under the Casey arccos transformation, and under an arctan transformation from the real projective line of cross-ratios to the unit circle. The cross-ratio distributions are novel to the probability literature; surprisingly various types of Cauchy distribution appear. To gain some analytical insight into the distribution, a simple linear-ratio is also introduced. We also give some results for the projective invariants of five coplanar points. We discuss the general moment properties of the cross-ratio, and consider some inference problems, including maximum likelihood estimation of the parameters.

Distributions of projective invariants and model-based machine vision

In machine vision, objects are observed subject to an unknown projective transformation, and it is usual to use projective invariants for either testing for a false alarm or for classifying an object. For four collinear points, the cross-ratio is the simplest statistic which is invariant under projective transformations. We obtain the distribution of the cross-ratio under the Gaussian error model with different means. The case of identical means, which has appeared previously in the literature, is derived as a particular case. Various alternative forms of the cross-ratio density are obtained, e.g. under the Casey arccos transformation, and under an arctan transformation from the real projective line of cross-ratios to the unit circle. The cross-ratio distributions are novel to the probability literature; surprisingly various types of Cauchy distribution appear. To gain some analytical insight into the distribution, a simple linear-ratio is also introduced. We also give some results for the projective invariants of five coplanar points. We discuss the general moment properties of the cross-ratio, and consider some inference problems, including maximum likelihood estimation of the parameters.