KERANGKA KERJA PENENTUAN VOLUME TELUR MENGGUNAKAN COMPUTER VISION DAN ATURAN SIMPSON

Volume has a very important role in the production and processing of food products. Egg volume is associated with egg composition, nesting success, hatchling size, and nesting period. This paper develops a framework for egg volume measurement using computer vision and Simpson’s rule. The framework consists of image acquisition, preprocessing, image segmentation, image rotation, and volume measurement using Simpson’s rule. Simulation has been done using circle images and ellipse images with several diameters and major and minor axis respectively. The simulation result shows that volume measurement using Simpson’s rule is more accurate than volume measurement using sum of cylinder volume

Volume Measurement Algorithm for Food Product with Irregular Shape using Computer Vision based on Monte Carlo Method

Volume is one of important issues in the production and processing of food product. Traditionally, volume measurement can be performed using water displacement method based on Archimedes' principle. Water displacement method is inaccurate and considered as destructive method. Computer vision offers an accurate and nondestructive method in measuring volume of food product. This paper proposes algorithm for volume measurement of irregular shape food product using computer vision based on Monte Carlo method. Five images of object were acquired from five different views and then processed to obtain the silhouettes of object. From the silhouettes of object, Monte Carlo method was performed to approximate the volume of object. The simulation result shows that the algorithm produced high accuracy and precision for volume measurement

A New Framework for Measuring Volume of Axisymmetric Food Products using Computer Vision System Based on Cubic Spline Interpolation

Volume is an important factor to determine the external quality of a food product. The volume measurement of food product is not a simple process if it is performed manually. For alternative, several volume measurement methods for food products have been proposed using 2D and 3D computer vision. Disk method and frustum cone method have been applied in many 2D computer visions to approximate the volume of axisymmetric food products. These methods were less in accuracy, since it used piecewise linear function to approximate the boundary of the object. This paper aims to propose a new framework for measuring the volume of axisymmetric food product based on cubic spline interpolation. Cubic spline interpolation is employed to construct a piecewise continuous polynomial of the boundary of object from captured image. The polynomial is then integrated to approximate the volume of the object. The simulation result shows that the proposed framework produced accurate volume measurement result

A virtual environment for the simulation of 3D wood strands in multiple view systems for the particle size measurements

In this paper, we present a complete virtual environment for the computation of synthetic three-dimensional samples representing free falling wood strands. The proposed method permits to simulate acquisitions performed by real multiple view setups in which the stream of strands falling out of a conveyor belt is analyzed with image processing techniques in order to compute the particle size distribution. Unfortunately, experiments in real time applications are complex and expensive, and the ground true is almost impossible to measure in such conditions. The creation of a metric and fully virtual environment of falling wood strands represent a key feature in order to properly design the illuminotecnic and optical setups, optimize the image processing methods as well as the three- dimensional reconstruction techniques, using controlled and fully repeatable virtual image datasets

Low-cost volume estimation by two-view acquisitions: a computational intelligence approach

The estimation of the volume occupied by an object is an important task in the fields of granulometry, quality control, and archaeology. An accurate and well know technique for the volume measurement is based on the Archimedes' principle. However, in many applications it is not possible to use this technique and faster contact-less techniques based on image processing or laser scanning should be adopted. In this work, we propose a low-cost approach for the volume estimation of different kinds of objects by using a two-view vision approach. The method first computes a reduced threedimensional model from a single couple of images, then extracts a series of features from the obtained model. Lastly, the features are processed using a computational intelligence approach, which is able to learn the relation between the features and the volume of the captured object, in order to estimate the volume independently of its position and angle, and without computing a full three-dimensional model. Results show that the approach is feasible and can obtain an accurate volume estimation. Compared to the direct computation of the volume from the three-dimensional models, the approach is more accurate and also less dependent to the position and angle of the measured objects with respect to the cameras

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method