Identify Toxin Contamination in Peanuts Using the Development of Machine Vision Based on Image Processing Technique

This research aimed at identifying the use of image processing technique and classifying the use of K-mean clustering of contaminated and uncontaminated peanuts. A machine vision system was made of a small aluminum box, equipped with a camera, a petri for placing the sample, USB connector, UV lamp, and a computer. Image processing methods consisted of analysis of the average color of RGB in the region of interest, convertion of RGB into HSV, segmentation process, as well as convertion image into grayscale and binary objects in order to obtain the total number of pixels value in the object area so that the mean value of the pixels of the area can be calculated. K-means algorithm was used to classify the contaminated and uncontaminated peanuts based on the average pixel value of R,G, B color parameters. The accuracy of a system was 100% meaning that the performance of machine vision can be used to identify the contaminated and uncontaminated peanuts.Keywords: aflatoxin, K-mean clustering, machine vision, image processing, peanuts

The Role of Tinted Lenses in the Perception of Color Deficiency

Purpose: Many different approaches have been used to improve the color perception in patients with color deficiency. Currently, filters and tinted contact lenses are the most widely-available method to enhance color perception. Filters have been shown to help patients to pass pseudoisochromatic color tests, to better perform their daily activities, and enable them to better enjoy nature and art as well. The aim of this study was to evaluate the efficacy of monocular Neuchroma tinted lenses in improving the color perception for those with color vision deficiency. The specific combination of tints was selected to mimic the neutral (black) point for protans and deutans to optimize contrast. These were compared to neutral density filters as a control condition, and a red filter as the established compensatory treatment for red-green color deficiency. Methods: 12 subjects (three females and nine males) with red-green defect, aged 14-88 years, were involved in this study. Subjects were asked first to perform Farnsworth-Munsell 100 hue wearing no tint. Then, participants then performed the digitial ColorDx test four times, with different filters. We used R3+RW3+RD3, IR3, neutral density and red filters in front of the right eye for protans, and IR3+RW3+RD3, LV2+LV3, neutral density and red filters for deutans. Each filter was put inside a black mask to make their appearance appear as similar as possible. After that, participants performed Farnsworth-Munsell 100-hue test for a second time with filter that caused minimal errors on ColorDx test placed in front of their right eye. Results: We found no statistical difference between neutral density and NeuChroma lens IR3 for protans, (p= 1.000). VL2+VL3 did not give a different result than ND (p= 0.339) for deutans. There was no statistical different between neutral density lens and IR3+RD3+RW3 NeuChroma lenses on ColorDx for all subjects, (p= 0.694). There was a remarkable reduction in total error scores on ColorDx test with the red filter (p = 0.003). Total error score on FM 100-hue test significantly increased with the red filter (p = 0.009). Conclusion: The results showed that tinted lenses has no significant impact on color vision defect subjects. The red filter improved color vision performance in three deutan subjects and shifted the defect from mild protan to mild deutan in one subject

Aplikasi Sistem Monitoring Pertumbuhan Tanaman Berbasis Web Menggunakan Machine Vision

Nowadays, demand for integrating between information technology (IT) and development of agricultural system isin order to increase the productivity, efficiency and profitability in term of precision agriculture. This matter occurred due to some problems in the field, such as; unintensively monitoring activities for plant during the growing period. One of the alternative solutions to overcome the problem was introducing the machine vision technology in the farming system. The research is actually as a basic research that aims using technology of digital image processing and software of computation (mathematics) to support a function of real-time monitoring system for plant growing. The research mechanism was started from digital image processing by using an image segmentation method that can identify between the main object (plant) and others (soil, weed). Image processing algorithm used excess color method and color normalization to identify plants, to calculate crop area. Otsu method was used to convert it to binary images. The next was to calculate and analyze a percentage of the plant growing, from after planting until harvesting time. The analyzed data were stored as MySQL database format in the web server. Final output of the research was the web based monitoring instruments for plant growing that can be accessed through intranet (local area network) as well as internet technology. From the software testing, monitoring with a machine vision system has a success rate reached 70 % for identifying plants.ABSTRAKTuntutan integrasi teknologi sistem informasi dan sistem pertanian saat ini dimaksudkan guna mendukung efisiensi,produktivitas dan profitabiltas pertanian. Hal tersebut didorong oleh timbulnya permasalahan di lapangan terkait dengan belum optimalnya produktivitas tanaman yang diakibatkan antara lain, kurang intensifnya pemantauan (monitoring) tanaman pada masa pertumbuhan. Salah satu alternatif solusi untuk memperbaiki permasalahan tersebut dengan mengaplikasikan teknologi machine vision. Penelitian yang dilakukan merupakan penelitian dasar yang bertujuan memanfaatkan teknologi pengolahan citra digital dan perangkat lunak komputasi untuk mendukung fungsi monitoring pertumbuhan tanaman secara real-time. Mekanisme penelitian dimulai dengan tahap pengolahan citra digital yang menggunakan metode segmentasi untuk mengenali objek tanaman dengan objek lainnya. Algoritma pengolahan citra menggunakan metode kelebihan hijau dan normalisasi warna, sedangkan untuk menghitung luas tanaman digunakan metode  Otsu  dengan  mengubah  ke  citra  biner. Tahap  berikutnya  menghitung  prosentase  pertumbuhan  tanaman selama proses budidaya sampai dengan panen. Data hasil pencitraan disimpan dalam basisdata MySql. Hasil akhir dari pengolahan data ditampilkan sebagai informasi pertumbuhan tanaman yang ditampilkan di website. Dari hasil pengujian, sistem monitoring dengan machine vision ini memiliki tingkat keberhasilan mencapai 70 % dalam mengenali tanaman

Research on robust salient object extraction in image

制度:新 ; 文部省報告番号:甲2641号 ; 学位の種類:博士(工学) ; 授与年月日:2008/3/15 ; 早大学位記番号:新480

Videoanalyysipalvelun ohjelmistoalustan suunnittelu ja suorituskyvyn arviointi

Video analysis is the programmatic observation of features in a video stream. This thesis designs a software platform which acts as a host for multiple video analyzer applications. The objectives are to allow effortless integration of analyzers such that dependencies between algorithms can be satisfied automatically, provide the analysis functionality over the internet as a service which can act as the engine for client applications, and do this integration in a manner which does not form a bottleneck for the analysis process. The research question is how to build a platform for integrating the analyzers in a way that makes integration easy and achieves good performance. The thesis consists of gathering requirements for the system, a review of related literature, a description of the design and evaluation and discussion of the designed system from the viewpoints of functionality, performance and architecture. The specification devised for the system defines it at least initially as more of a service to be utilized by the backends of client applications than a scalable content delivery network -like system, and it is emphasized that integration of various heterogeneous analyzers must be easy. Previous literature describes video analysis systems also operating in the cloud, but only ones tailored for a specific purpose and involving only a single analyzer. To make integrating new analyzers easy, the system designed here features the main ideas of allowing analyzers to run in Docker containers and register themselves with the platform at runtime with the platform determining analysis execution order based on information declared at registration-time. For performance, memory is shared between the platform and analyzers to avoid redundant operations. The platform provides good enough performance, not forming a bottleneck to the operation of the tested analyzer despite a loose approach to coupling, but tests with multiple analyzers operating concurrently would be needed to form a full understanding of the performance. The automatic resolution of dependencies based on requirements declared by analyzers is a novel way of allowing easy integration, and would likely be of use even in versions of the system developed vastly further. The REST API of the produced system is sufficient to facilitate the development of client applications. The stated goals are met, but actual implementation of client applications utilizing the platform would allow better assessment of the fitness of provided functionality. Tests of performance with more analyzers are needed, and if it proves to be lacking, there may be cause for replacing parts of the platform with ones utilizing computing resources more efficiently, or even designing a more tightly coupled analysis architecture operating as a single process.Videoanalyysi on kuvavirrassa esiintyvien kohteiden ohjelmallista havainnointia. Tässä diplomityössä suunnitellaan ohjelmisto toimimaan alustana useille videoanalyysisovelluksille. Tavoitteina ovat analyysisovellusten vaivaton integrointi siten, että algoritmien väliset riippuvuudet voidaan tyydyttää automaattisesti, analyysin tarjoaminen verkon yli palveluna joka voi toimia asiakassovellusten kehitystasoisena taustajärjestelmänä, ja integroinnin toteutus tavalla joka ei rajoita analyysiprosessin suoritusnopeutta. Tutkimuskysymys on, kuinka rakentaa alusta analysoijille tavalla, joka tekee integroinnista helppoa ja saavuttaa hyvän suorituskyvyn. Työ koostuu järjestelmän vaatimusten keräämisestä, katsauksesta aiheeseen liittyvään kirjallisuuteen, suunnitellun järjestelmän kuvauksesta ja sen arvioinnista toiminnallisuuden, suorituskyvyn ja arkkitehtuurin näkökulmista. Järjestelmälle laadittu määrittely asemoi sen ainakin aluksi ennemmin asiakassovellusten taustajärjestelmien hyödynnettäväksi kuin skaalautuvaksi sisällönjakeluverkon kaltaiseksi järjestelmäksi, ja eri heterogeenisten analysoijien integroinnin helppouden vaatimusta korostetaan. Aiempi kirjallisuus kuvaa myös pilvessä toimivia videoanalyysijärjestelmiä, mutta vain tiettyyn tarkoitukseen räätälöityjä yhtä analysoijaa hyödyntäviä. Jotta uusien analysoijien integrointi olisi helppoa, suunniteltava järjestelmä perustuu analysoijien ajoon Docker-konteissa ja itserekisteröitymiseen alustaan ajonaikaisesti alustan määrittäessä analyysisuoritusjärjestyksen rekisteröityessä annettuun tietoon perustuen. Suorituskyvyn vuoksi jaetaan muistia alustan ja analysoijien välillä, jotta päällekkäiset toiminnot vältettäisiin. Alusta tarjoaa tarpeeksi hyvän suorituskyvyn, eikä löyhästä kytkennästä huolimatta muodosta pullonkaulaa testatun analysoijan toimintaan, mutta täyden suorituskykykäsityksen saavuttamiseksi tarvittaisiin testejä useilla yhtäaikaisesti toimivilla analysoijilla. Algoritmien määrittämiin vaatimuksiin perustuva automaattinen riippuvuuksien selvitys on aiemmin käyttämätön tapa mahdollistaa helppo integrointi, ja olisi todennäköisesti hyödyllinen myös pitemmälle jatkokehitetyissä järjestelmän versioissa. Tuotetun järjestelmän REST-ohjelmointirajapinta on riittävä sallimaan asiakassovellusten kehitys. Määritellyt tavoitteet saavutettiin, mutta todellisten alustaa hyödyntävien asiakassovellusten toteutus sallisi paremman tarjotun toiminnallisuuden soveltuvuuden arvioinnin. Suorituskyvyn testaus useammilla analysoijilla on tarpeen, ja jos se osoittautuu puutteelliseksi, voi olla tarpeellista korvata järjestelmän osia suoritusresursseja tehokkaammin hyödyntävillä, tai jopa suunnitella tiukemmin kytketty yhtenä prosessina toimiva analyysiarkkitehtuuri