Mengenai hidup di Perancis dan orang Perancis yang birokratis

Bosen ngomongin Machine Vision terus, sekarang saya akan mencoba berbagi hal lainnya yaitu mengenai Perancis.

Setelah belajar dan bekerja di Perancis selama beberapa lama,saya akhirnya menyadari bahwa negara ini (dan orang-orangnya) banyak sekali yang menyebalkan.

Civil Servant Perancis itu pemalas dan birokratis. In fact istilah 'bureaucracy' itu asalnya dari Perancis (bureau itu artinya meja atau kantor). Disini semua urusan, hingga yang paling sederhana (misal beli abonemen tiket) harus melalu birokrasi yang panjang dan berbelit-belit. Di UK dan Belanda misalnya, beli tiket Abonemen tinggal datang ke loket beli dan selesai. Disini anda harus menyediakan copy attestation de logement(surat tempat tinggal), attestation de travail(surat pernyataan bekerja), copy identitas, dsb dsb.

Demikian juga untuk social security misalnya, di UK ketika anda terdaftar sebagai student/worker, secara OTOMATIS anda akan mendapatkan social security number (istilahnya National Health System di UK). Di Perancis anda harus mendaftar ke LMDE (student) atau CPAM (worker dan umum) dan prosesnya sangat panjang. Pertama anda harus mengisi formulir tebal, kemudian anda menyediakan berbagai macam dokumen penunjang, seperti medical result, ID, Releve Identite Bancaire, Titre De Sejour, slip gaji dari perusahaan dan berbagai macam dokumen lain baik copy maupun original.

Jika anda orang asing, dan mau bekerja di Perancis, proses-proses untuk mendapatkan work authorization itu akan sangat menyita waktu dan membuat kepala anda pecah. Untungnya saya terbantu karena perusahaan saya menyewa agen untuk mengurus masalah ini. Dengan bantuan agen aja sudah membuat kepala anda pecah, apalagi tanpa agen.

Jika anda mau menyewa residence, baik di Unversitas maupun di general residence, prosesnya juga tidak mudah. Untuk mendapatkan residence di Universitas, anda harus mengisi setumpuk dokumen, juga yang paling penting mendapatkan le Garant, atau penjamin (orang Perancis) yang mengisi setumpuk dokumen Acte De Solidarite menjamin bahwa anda membayar sewa. Mendapatkan residence di kota besar seperti Paris apalagi adalah hal yang hampir impossible bagi orang asing. Ada baiknya anda minta bantuan agen properti (Agence Immobiliere).

Sebenernya birokrasi tidak masalah jika saja para Civil Servant di Perancis itu rajin dan mau membantu, sayangnya kelakuan umum para Civil Servant di Perancis itu umumnya masa bodoh dan malas.

Kata-kata yang paling sering mereka ucapkan adalah "ce n'est pas possible" atau "non" Anda harus siap berdebat (dengan bahasa Perancis) dengan mereka jika ingin urusan selesai. Tetapi anda harus tetap memasang wajah ramah dan jangan lupa mengucapkan Merci Beaucoup walaupun sebenernya anda pingin mengetok kepala batu mereka dengan Roti pentung keras ala Perancis. Ingat jika anda terlihat jengkel, mereka justru akan semakin mempersulit hidup anda. Mindset orang Perancis adalah, dia baru akan membantu anda setelah anda kelihatan putus asa dan benar-benar berharap ke dia.

Pernah suatu ketika saya menyerahkan suatu dokumen ke ibu-ibu civil servant jutek ala Perancis dan dia cuman bilang "Ce n'est pas valide!". Beberapa minggu kemudian saya serahkan dokumen yang sama pada orang yang sama dan si bodoh ini menerima dokumen saya (rupanya dia lupa bahwa itu dokumen yang sama yang pernah dia tolak). So kesimpulannya suasana hati para Civil Servant ini mempengaruhi kesuksesan permohonan anda.

Jika anda orang asing, urusan akan berlipat-lipat kesulitannya, pertama karena kendala bahasa, kedua karena anda orang asing!

Bangsa Perancis adalah bangsa yang chauvinist, hal ini diperparah dengan fakta bahwa imigran-imigran dari negara Francophone (Afrika dan Arab) benar-benar membebani Ekonomi Perancis karena memang mereka kurang produktif, sementara sistem security social di Perancis harus menanggung mereka. Akibatnya bangsa Perancis kurang welcome dengan kehadiran bangsa lain. Saya merasakan bahwa urusan-urusan birokrasi jauh lebih sulit kalau saja tidak dibantu oleh teman atau atasan saya yang orang Perancis, bukan hanya masalah bahasa, tapi juga sepertinya masalah anda orang asing atau bukan.


Walaupun birokrasi Perancis ini merupakan keluhan umum bukan saja orang asing tapi orang Perancis sendiri, tapi tetap saja orang asing lebih sulit. Dalam satu kasus bahkan birokrasi Perancis ini pernah menyebabkan kematian! seperti dilansir oleh koran Guardian berikut :

"One of the most unforgivable aspects of France's obsession with paperwork, however, came to light in the fire that killed seven last night. Twelve of the 22 families who lived in a building that was, by common acknowledgement, a death trap, were rehoused a few months ago.

The remaining 10, who were in the building when it went up in flames, were not - because they did not have the correct papers to justify their residence in France."



Selain birokrasi,pengalaman saya bekerja di Perusahaan Perancis adalah orang Perancis itu suka sekali berdebat dan berargumentasi, walaupun nyata-nyata bahwa kita lebih expert dari dia.

Mereka baru diam setelah kita menunjukkan bukti scientific, tentu saja hal ini menyebabkan pekerjaan tertunda karena waktu habis hanya untuk berdebat.

Untungnya walaupun pemalas, tidak bisa berbahasa Inggris dan mikirnya birokratis dan complicated, bangsa Perancis dikaruniai Tuhan kemampuan matematika, kreativitas dan kemampuan berinovasi yang tiada duanya. Kemampuan matematika dan kreativitas bangsa Perancis diakui dari kemampuan mereka menciptakan teori matematika seperti yang dibuat oleh Fourier, Fibonacci, Fermat, Laplace, Poincere, Descartes....

Juga keindahan dan kompleksitas bangunan-bangunannya dan kemampuan mereka membuat karya engineering yang complicated seperti pesawat tempur Mirage, Rafale, pesawat Supersonic Concorde dan kereta supercepat TGV (Train Grande Vitesse).

Riset-riset Perancis di bidang teknologi juga cukup advanced dan bisa bersaing dengan negara-negara maju lain seperti US, UK, Jerman dan Jepang.

Dalam pendidikan postgraduate misalnya, Bangsa Perancis memiliki inovasi tersendiri yaitu Industrial PhD atau disebut konvensi CIFRE (Les Conventions Industrielles de Formation par la REcherche) yaitu skema yang memungkinkan research engineer melakukan riset doktoral di Universitas sambil menerapkannya di industry dan mendapatkan gelar PhD.

Skema CIFRE inilah yang menyebabkan saya memilih tinggal dan bekerja di Perancis dibandingkan negara lain. Di Perancis kita bisa bekerja di Industry, sekaligus melakukan riset PhD di Universitas. So sambil menyelam minum air.

Selain itu, tidak ada yang istimewa di Perancis. Oh ya sebenarnya ada satu yang istimewa disini, yaitu cewek-ceweknya yang memang bisa dibilang 99% cantik. But these stuck up bitches cannot speak English and they adore Salvatore Ferragamo, Etienne Aigner, Paul Smith and Chloe more than God plus they are unfaithful! ....so these goddesses are not a good deal..

Ok itu dulu, à bientôt...

3D laser scanning

1. Principle of 3D laser scanning


Sometimes we need to analyze a real world object to gather the data of its shape, appearance (color, texture), or dimensional measurement. In order to do that we need a 3D scanner. With 3D Scanner we can gather the data. The data then can be used to reconstruct digital 3D model useful for many different applications. 3D reconstruction used extensively in the production of movie and game, as well as industrial design, quality control, forensic, reverse engineering and
prototyping, computer vision and documentation of cultural or heritage artifacts.

Now the principle of 3D scanning will be explained. The device called 3D scanner will be used to create a point cloud of geometric samples on the surface of the object. These points then will be used to extrapolate the object shape (this is termed 3D reconstruction), the point can also be consisted of the color information, in this case the color on the surface of the object can be determined as well.

3D scanner collects information about surface. 3D scanners keep and collect
the distance information about surfaces within its field of view. The image gathered by 3D scanner describes the distance to a surface at each point in the picture. If a spherical coordinate system is defined in which the scanner is the origin and the vector out from the front of the scanner is φ=0 and θ=0, then each point in the picture is associated with a φ and θ.

Together with distance, which corresponds to the r component, these spherical coordinates fully describe the three dimensional position of each point in the picture, in a local coordinate system relative to the scanner. In many cases, single scan from one angle will not generate a complete model of the object. We need to take multiple scans on multiple scans from different angles to gather information about all sides of the object. These scans then transformed into some reference
system and a process termed image registration will be applied to obtain a complete model [wikipedia].


A scanner will emit laser and detect its reflection in order to probe an object. There are few types of laser scanner, two famous laser scanner type are time of flight laser scanner and triangulation laser scanner.

The first type of 3D laser scanner is the time-of-flight 3D laser scanner. This type of active 3D scanner utilizing laser light to probe the object. The principle behind this laser scanner type (also known as laser range finder) is using laser to emit a pulse of light and calculating the distance between the scanner and the surface of the object by measuring the time of the round-trip-time of a pulse of light. Since the speed of light c is known, the roundtrip time determines the travel distance of the light, which is twice the distance between the scanner and the surface. If t is the round-trip time, then distance is equal to ct/2.

The accuracy of a time-of-flight 3D laser scanner depends on how precisely we can measure the time: 3.3 picosecond approximately is the time taken for light to travel 1 millimeter. The laser range finder only detects the distance of one point in one direction (the direction of its current view). Therefore, the scanner scans its entire field of view one point at a time by changing the direction of range finder’s to scan different points. The direction can be changed by either rotating the range finder itself or using rotated mirror systems. The rotated mirror methods are
faster and more accurate because mirrors are lighter. Generally time-of-flight 3D laser scanners can measure the distance of 10.000-100.000 points per second.
This type of laser scanner is more suitable to scan a far object, like buildings, rock formations etc produce a 3D model. [Wikipedia]. One of application of this scanner is Lidar (light detection and ranging) scanner, the example of reconstructed object using this type of laser scanner is shown below :





The second type of laser scanner is triangulation laser scanner, it is also a active scanner that uses laser light to probe the environment. These laser scanners shines a laser on the object and take advantage of a camera to look for the location of the laser dots emitted. Depending on how far away the laser strikes a surface, the laser dot appears at different places in the camera’s field of view as shown below :




This technique is called triangulation because the laser dot, the camera and the laser emitter form a triangle. We get three informations :
1. The length of one side of the triangle, the distance between the camera and the laser emitter.
2. The angle of the laser emitter corner.
3. The angle of the camera corner (determined by looking at the location of the laser dot in the camera’s field of view).

With three information described above, we can entirely establish the shape and size of the triangle and gives the location of the laser dot corner of the triangle.
The triangulation process can be described as follows. A laser stripe (or line) approximately 2 ½ inches long is projected from the laser head onto the surface to be digitized.

The CCD cameras,at a known distance from the laser, then capture the light from the laser beam as it reflects on the part. Through trigonometry, the XYZ co-ordinates of the stripe can then be calculated.

Depending on software settings and the sensor used, up to 650 individual data points can be gathered from a single laser line. The object that was scanned will now be represented by these points, commonly referred to as a 'point cloud', numbering from a few thousand points up to possibly a million or more.

The number of points scanned on a part will depend on the size and amount of detail the part has. The more detail, the higher the number of points necessary to describe the part. [Steinbichler].

In recent development of this type of laser scanner a laser stripe, instead of a single laser dot, is scrutinized across the object to speed up the acquisition process.

Both types of scanner have its own advantage and drawbacks. They used on different purpose and situations. The advantage if time-of-flight laser scanner is that it is has a capability to scan a very long distance, sometimes in order of kilometers. This scanner is suitable for scanning large structure like building or geographic characteristic, the drawback of time of flight laser scanner is their accuracy. This is because of the high speed of light, making the round-trip time is difficult to calculate in the order of millimeters. With time of flight scanners accuracy is
decreased when the laser hits the edge of an object because the information that is sent back to the scanner is from two different locations for one laser pulse. The coordinate relative to the scanners position for a point that has hit the edge of an object will be calculated based on an average and therefore will put the point in the wrong place.

On the other hand, the advantage of triangulation laser scanner is its accuracy. They can only scan in limited range (order of meters) but their accuracy is in the order of tens of micrometers.

That is to conclude that triangulation laser is more suitable to reconstruct a small 3D object. When using a high resolution scan on an object the chances of the beam hitting an edge are increased and the resulting data will show noise just behind the edges of the object.

Scanners with a smaller beam width will help to solve this problem but will be limited by range as the beam width will increase over distance. Software can also help by determining that the first object to be hit by the laser beam should cancel out the second. Laser scanning shortens the digitizing process by collecting the data at a much faster pace than conventional measurement techniques. Scanning with the laser eliminates the issues relating to cosine error, deflection, mechanical probe offsets, and probe size and/or shape.


2. Optimization of 3D laser scanning for reflective surface

When the data from 3D laser scanner are reconstructed, the large data sets usually have to be processed. Hence, it is often necessary to minimize the number of points while minimizing the loss of information simultaneously. Moreover, the generated point cloud usually contains a considerable number of errors. The errors come from the measurement system and the scanned object’s surface. Outliers and other incorrect points are an important factor when discussing precision measurement.

Therefore, they have to be first detected and removed or corrected from the point cloud in order to get a clean model. Hence the data measuring can be done more
precisely.


Based on this fact, optimization of the laser scanning system can be realized if the data quality of each individual point in the data set is improved . This can be accomplished by considering the specific settings of the scanning system, particularly, the parameters of the laser and the camera. Optimizing their positions based on the results of the point cloud analysis results in
better point cloud quality in a successive scan.


There are many sources for incorrect scan data. Based on many numerous example scans, most of the outlier and other erroneous points are caused by reflections. In these cases, the high energy laser beam is reflected from mirroring surfaces such as metallic or glass. Consequently, there are too much light hits the sensor of the camera and blooming effects occur. In other cases, a direct reflection may miss the camera. In addition, a part of the object may lie in the path from the projected laser line to the camera causing a shadowing effect. All these effects are responsible for gaps and holes. At sharp edges of some objects, partial reflections appear. In addition, rough surfaces cause multiple reflections and, therefore, indefinite point correlations.

Other problems are caused by possible range differences originating from systematic range errors resulting from different reflectivity of the surfaces elements. Since the scanner systems are typically used in industrial environments, some atmospheric effects like dust and dirt possibly affect the quality of the image obtained by the camera. Furthermore, aliasing effects in the 2D image processing lead to high frequent noise in the generated 3D data.

Therefore, the resulting 3D point data is noisy and partially erroneous. However, a lot of these errors can be minimized by an optimal alignment of the projection system and the object surface so that as few as possible reflections can appear. In order to arrange the setup properly, the quality of the generated point cloud has to be analyzed and evaluated.

The steps to get optimized 3D reconstruction by improving point cloud quality are :

1. Optimize the projection and viewing conditions. This way, the quality of the point cloud has to be quantified with respect to the position of laser and camera. Improving the recording conditions will result in less erroneous 3D points.

The proposed techniques for improving the recording condition are based on the system’s parameters in 2D (e. g., contrast and line thickness) and in 3D (e. g., camera and laser positions, focus area, etc.) to estimate the quality of each single point [Teusch 2004].

2. After the point clouds is quantified, now we have to correcting a single measurements of a point. The goal is to minimize the number of points and clean the point clouds from noise. The algorithm to do it is by analyzing the points using B-Spline approximations. The point clouds are approximated by a sorted set of B-spline curves for iterative smoothing and closing gaps [Teusch 2004]. The edge information is derived from these curves and then reconstructed by using NURBS curves with respect to quality and curvature of each single point on it. NURBS or
Non-Uniform Rational B-Splines, is a mathematical representations of 3-D geometry that can accurately describe any shape from a simple 2D line [Fisher 2002].

3. Now the triangulation algorithm can be conducted as usual.
The figure below show the example result of this method:

The comparison of Pixel and Vector Based Image Processing

There are few methods of Surface Inspection using pixel based method, namely :
1. Traditional Object Recognition: this methods is identifies object by generalizing object's image pattern. The first step is extraction the features of the object and then train the system according to the features by using classifier algorithm, such as Neural Network or SVM. This method will face different challenges, such as : illumination variance and distribution, object surface characteristics, orientation and occlusion

2. Blob/Particle Analysis : This methods will extract any object in the surface by separate it from the background
then group it accordingly to form a blob.
The geometry of the blobs is then used for surface detection. This is very fast and practical method for simple surface
inspection. The disadvantage of this method is that separation of the object from the background is not an easy task
due to noise as a result of change of color, scratch, marking etc.

3. Template Matching with Normalized Grey Scale correlation :
In this method the template is stored and then matched with the object inspected with some correlation method.

The advantage of this method is that it is much more accurate and more robust than the previous two methods. The added advantages are that it is relatively easy to train an object and that the object does not need to be separated from
the background. The disadvantages of such a method are that it cannot handle much variation in rotation and size and is highly affected by non-uniform shading. In the application of inprocess inspection in industry, changes in rotation and size as well as non-uniform shading is the standard rather than the exception.

Now in contrast, the vector based image processing will be described and compared. In the previous three methods described above, the image processing are applied on the basis of pixel by pixel, hence pixel based image processing.

This method is slow and challenged by problems in different illumination, pose and occlusions.

Vector based image processing is totally different that it converts all the pixel into geometric features by means of synthetics or mathematical model.The geometric feature can be line segments, arcs, angles and open or closed geometric shapes.
By using geometric features, the image analysis is not affected by color changes or non-linear changes in size such as those found with components due to manufacturing variations. This method is also robust against variance in shading an non-linear lighting.

Intel OpenCV, alternatif Opensource Machine Vision

Kalau anda atau perusahaan anda tidak cukup punya dana untuk membeli Software NI Development Module atau Cognex VisionPro yang harganya berkisar dari 5000-20.000 USD, anda bisa mempertimbangkan OpenCV dari Intel sebagai alternatif solusi. Library open source ini bisa di-download dari

www.intel.com/technology/computing/opencv/

atau

http://sourceforge.net/projects/opencvlibrary

OpenCV adalah library Open Source untuk Computer Vision untuk C/C++, OpenCV didesain untuk aplikasi real-time, memiliki fungsi-fungsi akuisisi yang baik untuk image/video. OpenCV juga menyediakan interface ke Integrated Performance Primitives (IPP) Intel sehingga jika anda bisa mengoptimasi aplikasi Vision anda jika menggunakan prosesor Intel.

Feature yang dimiliki OpenCV antara lain :

* Manipulation data citra (alokasi, copying, setting, konversi).
* Citra dan video I/O (file dan kamera based input, image/video file output).
* Manipulasi Matriks dan Vektor beserta rutin-rutin aljabar linear (products, solvers, eigenvalues, SVD).
* Data struktur dinamis (lists, queues, sets, trees, graphs).
* Pemroses Citra fundamental (filtering, edge detection, corner detection, sampling and interpolation, color conversion, morphological operations, histograms, image pyramids).
* Analisis struktur(connected components, contour processing, distance transform, various moments, template matching, Hough transform, polygonal approximation, line fitting, ellipse fitting, Delaunay triangulation).
* Kalibrasi kamera (calibration patterns, estimasi fundamental matrix, estimasi homography, stereo correspondence).
* Analisis gerakan (optical flow, segmentation, tracking).
* Pengenalan obyek (eigen-methods, HMM).
* Graphical User Interface (display image/video, penanganan keyboard dan mouse handling, scroll-bars).
* Pelabelan citra (line, conic, polygon, text drawing)


OpenCV terdiri dari 3 library, yaitu
CV : untuk algoritma Image processing dan Vision
Highgui :untuk GUI, Image dan Video I/O
CXCORE : Untuk struktur data, support XML dan fungsi-fungsu grafis.


Selain itu OpenCV juga dilengkapi dengan Machine Learning library yang memiliki algoritma berikut :

* Naive Bayes classifier
* k-nearest neighbor algorithm
* Support Vector Machine
* Decision Trees
* Boosting
* Random forest
* Expectation Maximization
* Neural Networks


Library ini bisa di-compile dengan Visual C++ 6.0, Visual C++.Net 2003/2005/2008 baik Standard, Professional maupun express edition.

Sepengalaman saya, OpenCV memang tidak semudah dan se-robust NI Vision Development Module atau Cognex Visionpro, tapi dalam beberapa kasus justru lebih fleksibel dan extendable. Hanya saja memang anda harus benar-benar jago coding untuk dapat menggunakan OpenCV. Karena Library ini sangat ringkih dan rentan terhadap memory leak. So management memory anda harus benar-benar baik.

Lain kali saya akan sajikan tutorial OpenCV mulai dari instalasi sampai pembuatan aplikasi Computer Vision sederhana.

NI Vision Development Suite

National Instruments Vision Development Module (www.ni.com/vision/vdm.htm) adalah tools yang sangat luar biasa untuk pengembangan aplikasi Machine Vision. Ini adalah sebuah development module yang sangat lengkap dengan berbagai library yang bisa di-porting ke VB.Net, Labview dan CVI/Labwindows dan C/C++, modul ini juga memiliki prototyping tool yang disebut Vision Assistant, dengan tool ini, kita dapat dengan mudah membuat prototype program machine vision dan mengganti-ganti image processing algorithm atau filter yang digunakan. Kalau kita mendevelop aplikasi computer vision sederhana menggunakan Matlab dengan Image Processing toolbox atau OpenCV, akan memakan waktu berjam-jam, dengan Vision Assistant hanya memakan waktu kurang dari satu jam.

Program ini mengakuisisi data, baik live image dari Frame board maupun static image, kemudian kita konversi ke 8 bit image atau binary. Dari situ kita bisa melakukan manipulasi image dengan mengganti-ganti filter untuk memperhalus image, merubah kontras, brighness, mendeteksi edge, segmentasi, transformasi ke domain frekuensi dan lain-lain . Selanjutnya setelah feature dari image kita dapatkan, kita bisa melakukan measurement, pattern classification/object detection dan blob/particle analysis.

Setelah puas dengan prototype program yang kita buat, kita bisa memporting prototype program ke Visual Basic, Labview, atau Labwindows/CVI untuk dimanipulasi lebih jauh atau digabung dengan library lain.

Sangat praktis.

CVI/Labwindows : Alternative bagi pembenci Labview

Bagi anda-anda yang berkecimpung di Control Systems/Instrumentation, pasti kenal dengan program yang satu ini, ya Labview dari National Instruments. Di Perancis sini, hampir semua Industri memakai Labview sebagai bahasa utama pengembangan sistem instrumentasi dan kontrol di industri. Di tingkat universitas dan ecole (institut/sekolah tinggi), setiap mahasiswa Teknik Elektro dan Ilmu Komputer (Informatique) di Perancis diwajibkan untuk mempelajari Labview sebagai bagian dari matakuliah pemrograman, Sistem Kontrol dan Instrumentasi. FYI uniknya di Perancis, mahasiswa Ilmu Komputer atau seringnya disebut Industrielle Informatique disini SELALU mendapatkan mata kuliah Control Systems dan Instrumentation. Sesuatu yang tidak saya temui di UK dan Indonesia. Hal ini disebabkan oleh banyaknya industri manufaktur di Perancis sehingga demand untuk engineer yang menguasai Sistem Kontrol dan Automasi sangat tinggi.

Labview menggunakan konsep Graphics Language sebagai landasan pemrogramannya. So semua operasi logika, iterasi dan matematika direpresentasikan dengan lambang dan flowchart. Labview memang ideal untuk scientist dan engineer yang kurang kuat background programmingnya tapi harus membuat aplikasi measurement dan insrumentasi dengan cepat.

Ok anyway, bagi saya, meskipun sangat tepat untuk data acquisition and measurement, orang Perancis kebablasan menggunakan Labview sebagai bahasa pemrograman general untuk urusan APA SAJA. Bahkan teman saya disuruh menggunakan Labview untuk memodelkan persamaan matematik (dimana Matlab, Mathematica atau Scilab lebih tepat untuk itu).

Labview memaksa kita untuk menggunakan prinsip dataflow, dimana fleksibilitas menjadi taruhannya.

Untuk program-program sederhana seperti akuisisi data dari instrumen, mengukur, dan menampilkannya, graphical programming language ini sangat cepat dan praktis. Tapi untuk menulis program yang cukup kompleks (misal aplikasi Object Detector and Recognition) dibutuhkan fleksibilitas yang sangat tinggi.

Sebagai orang pure Computer Science yang berlatar belakang text based programming dan telah mempelajari Theory of Programming Language dan Compiler Design, saya sangat membenci Labview dengan alasan berikut :

1. Tidak bisa memberi komentar
2. Tidak bisa memberi nama variabel (WTF ??)
3. Nonlinear dan alurnya susah diikuti, ini bikin program atau main game Maze???
4. Untuk memodifikasi satu perintah (dimana dalam bahasa pemrograman text hanya merubah satu baris) harus merubah keseluruhan struktur program.
5. Nonlinearity membuat sangat sulit untuk di-debug.
6. Nonlinearity membuat timing menjadi hal yang sangat sulit.
7. Penanganan Array dan iterasi yang rumit.
9. Sulit untuk menerapkan konsep OOP.
10. Pendukung Labview bilang operasi Parallel lebih mudah divisualisasikan dengan Labview, BULLSHIT, saya membuat parallel program dengan Celoxica Handel-C, tidak ada masalah.

Pertama kali menggunakan Labview untuk aplikasi numerical simulation and modeling (karena perusahaan tidak memiliki Matlab dan bos taunya hanya Labview), saya banyak terbantu dengan memakai C-Script untuk formula programming. Tapi lama-lama setelah aplikasi jadi kompleks, ternyata gabungan Labview dengan C-Script membuat program jadi rumit dan berat.
Akhirnya, setelah berkutat dengan kejengkelan, VOILA saya menemukan solusinya, Paolo, seorang PhD EE asal Brazil yang juga membenci Labview memberitahu pada saya bahwa Labwindows/CVI bisa menggantikan Labview dan kebetulan perusahaan MEMILIKI LISENSI Labwindows/CVI!

Labwindows/CVI adalah ANSI-C Compiler buatan National Instruments yang diklaim pembuatnya dapat menggunakan semua library dan modul akusisi data dari National Instrument dan compatible dengan Labview. So kita bisa mengerjakan aplikasi instrumentasi dan kontrol yang sama dengan Labview tanpa kepusingan-kepusingan bahasa pemrograman grafis.

Akhirnya semua program Labview saya convert ke Labwindows/CVI dan saya bisa meneruskan pekerjaan saya dengan bahasa pemrograman tercinta, ANSI-C !!

Beberapa hari setelah bermain dengan Labwindows/CVI, saya menyadari bahwa development tool ini lebih friendly daripada Visual C++.Net dan tentu saja memiliki library instrumentasi yang sangat lengkap. So Labwindows/CVI adalah tools terbaik untuk aplikasi instrumentasi, kontrol dan measurement di Industry bagi para software engineer atau hardcore coder.

Keys on Machine Vision Selection

Modern Production and manufacturing process now rely on the use of machine vision technology for test, measurement and inspection task. Nowadays there are many machine vision vendors offering products with different features and applications. This paper will highlight some of the key attributes we should look for in a machine vision supplier, and why taking the time to carefully assess a supplier’s capability and choosing the right partner will save the time and money in the long run.

There are many factors to be considered when choosing a machine vision technology from specific vendors. The factors are :

The Experience of the company.

1. Hardware Features. The parameter considered are :
1. Raw Image quality : Resolutions, Contrast/Sharpness, SNR, can be parameterized by MTF/SFR.
2. Speed.
3. Ability to manipulate image in subpixel level.

2. Software features.

1. OS supported.
2. Performance.
3. Reliability.
4. Hardware supported.
5. The modularity of the SDK (ex. COM, Activex).
6. The flexibility of the SDK to be customized.

4. Compatibility with other products, for example lighting systems, mechanical systems, control systems.

5. After Sales Service and support. Parameters considered :
1. Inventory reserve for emergency order fulfillment
2. Long-term product availability for a technology migration path
3. Advanced replacements for immediate failure replacement
4. Priority technical support
5. Flexible product training- on-site or at vendor’s training center
6. Customized hardware and software solutions
7. Vision system application engineering consulting
8. On-line logistical information for order tracking and account management
9. In-depth on-line knowledge base and training tools