Purwarupa Pembersih Pipa Otomatis (Automatic Tube Remover) Pada Heat Exchanger Menggunakan Teknik Pengolahan Citra

In the industry era, especially the oil processing industry, the heat exchanger is needed to regulate temperature and produce oil products such as petroleum, kerosene and diesel fuel. In the operation, the heat exchanger requires maintenance, especially when the minor unit is shut down or stop and routine reparation. Maintenance is done by replacing the tubesheet that commonly referred to as bundle retube where there are pipe fritter to be cleaned, which are cutted at the time of maintenance. This maintenance is typically done in manual approach, which is not efficient in terms of time. For a more efficient maintanence, this paper proposes a prototype design to discard these pipes fritter by utilizing image processing method for detecting the edge of the circle and the position of the pipe fritter. Based on the experiments, it has been obtained that the test circle radius that can be captured is at 4 to 10 pixels. The longest time for positioning was 2.41 minutes and the whole process of disposal of this pipeline reaches 47.92 %

Multi-stereo camera system to enhance the position accuracy of image-guided surgery markers

The development of Image-guided Surgery (IGS) system as an assistant tool in medical navigation has led to new challenges for researchers to enhance the accuracy of the medical surgery. In IGS, a stereo camera is used to find the position of medical markers and visualize it on the screen of the surgeon. However, the line of sight (LOS) between the camera and the markers causes the stoppage of the tracking system if it cut during the operation. This paper presents a multi-stereo camera system to overcome the LOS problem, and to improve the accuracy of the IGS system. A pair of stereo cameras has been used to recognize and detect the reference markers and visualize a patient's body part and a surgical needle. A multi-stereo camera has generated a very good accuracy of 3D visualization with (2.88 mm) of root mean square error (RMSE). Image filtering techniques have been used to process the captured images. Thus, IGS system based on multi-stereo camera, contributes promising results of medical navigation and enhances the capabilities of IGS system

Uji Deteksi Objek Bentuk Bola Dengan Menerapkan Metode Circular Hough Transform

Abstrak— Deteksi objek bentuk bola merupakan salah satu penerapan dari teknologi image processing yang saat ini banyak digunakan untuk teknologi robotika. Kemampuan dalam mengenali objek tertentu dalam berbagai kondisi lingkungan merupakan salah satu syarat teknologi image processing ini disebut handal. Untuk mengetahui kehandalannya maka perlu dilakukan pengujian. Uji deteksi objek berwarna bentuk bola dilakukan dengan melakukan pengujian terhadap perubahan kondisi lingkungan dimana objek tersebut berada, diantaranya dengan pengujian deteksi objek bentuk bola dengan variasi ukuran bola, pengujian deteksi objek bentuk bola dengan variasi perubahan intensitas cahaya dan pengujian deteksi objek bentuk bola dengan variasi perubahan jarak objek terhadap kamera. Dengan tiga pengujian yang telah dilakukan dengan metode hough transform yang diterapkan pada deteksi objek bentuk bola ini, diperoleh kesimpulan bahwa deteksi objek mampu mengenali variasi ukuran bola dengan diameter 16,9mm, 31mm, 63,7mm dan 95,8mm. Deteksi objek mampu mengenali bola dengan baik pada intensitas cahaya antara 80lux – 117lux. Dan deteksi objek mampu mengenali bola pada jarak 30cm – 140cm

Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images

Iris centre localization in low-resolution visible images is a challenging problem in computer vision community due to noise, shadows, occlusions, pose variations, eye blinks, etc. This paper proposes an efficient method for determining iris centre in low-resolution images in the visible spectrum. Even low-cost consumer-grade webcams can be used for gaze tracking without any additional hardware. A two-stage algorithm is proposed for iris centre localization. The proposed method uses geometrical characteristics of the eye. In the first stage, a fast convolution based approach is used for obtaining the coarse location of iris centre (IC). The IC location is further refined in the second stage using boundary tracing and ellipse fitting. The algorithm has been evaluated in public databases like BioID, Gi4E and is found to outperform the state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201

A MODEL VISION OF SORTING SYSTEM APPLICATION USING ROBOTIC MANIPULATOR

Image processing in today’s world grabs massive attentions as it leads to possibilities of broaden application in many fields of high technology. The real challenge is how to improve existing sorting system in the Moduler Processing System (MPS) laboratirium which consists of four integrated stations of distribution, testing, processing and handling with a new image processing feature. Existing sorting method uses a set of inductive, capacitive and optical sensors do differentiate object color. This paper presents a mechatronics color sorting system solution with the application of image processing. Supported by OpenCV, image processing procedure senses the circular objects in an image captured in realtime by a webcam and then extracts color and position information out of it. This information is passed as a sequence of sorting commands to the manipulator (Mitsubishi Movemaster RV-M1) that does pick-and-place mechanism. Extensive testing proves that this color based object sorting system works 100% accurate under ideal condition in term of adequate illumination, circular objects’ shape and color. The circular objects tested for sorting are silver, red and black. For non-ideal condition, such as unspecified color the accuracy reduces to 80%.

Segmentation-free quantification of spots on a homogeneous background

Abstract—A recurrent problem in biological image analysis is to quantify the number and size of spots on a homogeneous back-ground. Most automated approaches rely on segmenting the individual spots, which becomes unreliable when the image contains artifacts, noise, or confounding objects. Therefore, practitioners often resort to tedious and time-consuming manual counting and measurements. As an alternative, we propose a visual analytics approach to this problem. It is based on Total Variation Flow, a par-tial differential equation which changes the intensities of image regions at a rate which is inverse to their scale. From this, we derive novel quantitative per-pixel measures of scale and density, and we show how the results can be combined with tools for visualization and selection to achieve a fast summary of median size and spot density in an image. Given a set of images, our framework places them on a 2D map that makes it easy to quickly compare them with respect to spot sizes and density. Our system is applied to real-world data from Stimulated Emission Depletion (STED) microscopy.