From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Bidang pengolahan citra digital mulai berkembang pesat sejak ditemukannya foto digital. Kemajuan ini tidak terlepas dengan semakin canggihnya teknologi digital yang menunjang pengolahan citra digital pada gambar diam. Saat ini masih ada masyarakat yang menyimpan dokumentasi berupa klise foto yang direkam atau dipotret pada saat dahulu. Studio foto yang bisa mengubah klise foto menjadi foto digital sudah jarang ditemui pada masa sekarang, sehingga penelitian ini dilakukan untuk melakukan pengolahan gambar pada klise foto untuk menjadi gambar digital true color. Kemampuan alat bantu komputer dalam menyelesaikan masalah sangat berperan untuk mengubah citra pada klise foto menjadi foto digital. Penelitian ini menerapkan metode citra negatif. Hasil penelitian ini menunjukan bahwa dengan metode citra negatif dapat menjadikan citra klise foto menjadi foto digital. Kata kunci : Pengolahan citra digital, Citra Klise foto, Foto digital.

https://jurnal.untirta.ac.id/index.php/VOLT/article/download/948/1938

Implementasi Metode Waterfall pada Proses Digitalisasi Citra Analog

Detection of objects in an image 2 dimensional is a process which is quite complex to do. Object detection required a computer vision approach to the desired part of the object can be recognizable computer accurately. The method in this research using waterfall method. This research will describe the application of color segmentation method with the color detection of a digital image to produce image segment object in the form of blob so that the computer can be detected. The object detection process will process the resulting color segment by the process of segmentation so that can know the number of detected objects, the area and the center of the object. This application can take pictures with laptop or notebook webcam. The result of color segmentation based on color detection is strongly influenced by color samples and color tolerance values to which the segmentation process is based. Lighting, location, texture, and contour of objects or background image will greatly affect the results of segmentation and object detection.

https://jurnal.untirta.ac.id/index.php/VOLT/article/download/1095/2057

Implementation Segmentation of Color Image with Detection of Color to Detect Object

A bstract - Biometrics is the study of automatic methods for recognizing humans based on one or more parts of the human body that are unique. One human characteristic that can be used is iris, iris features can be used as distinguishing characteristics with other individuals. The stage that the writer did to be able to recognize the iris pattern of someone's eye in a digital image was the pre-processing stage, the template saving stage and the matching stage. In this study the author applies the template matching method to store the image into a template image stored in the database and the algorithm correlation coefficient for the characteristic matching algorithm between template data and test data. The application is designed using the Matlab R2010a programming language. The results of testing 22 images obtained by the percentage of system success was 86.36%. Keywords - Iris, Template Matching, Correlation Coefficient

/pdf/pengenalan-iris-mata-menggunakan-metode-template-matching-1n5wj3euha.pdf

Pengenalan iris mata menggunakan metode template matching correlation

Abstrak - Pada penelitian ini, noise dibangkitkan dengan menggunakan tools matlab dan dilakukan analisa. oleh karena itu, untuk mereduksi derau (noise) perlu dilakukan suatu proses perbaikan kualitas citra terhadap citra yang mengalami derau atau noise tersebut sehingga citra dapat dengan mudah diinterpretasikan baik oleh manusia maupun mesin. Penelitian ini menggunakan metode harmonic mean filter. Harmonic mean filter merupakan salah satu metode untuk menghitung rata-rata dari citra yang rusak g(s,t) pada sebuah blok area citra yang didefinisikan oleh 𝑆𝑥𝑦. Nilai dari citra f(x,y) yang diperbaiki pada tiap titik (x,y) hanya dihitung dengan menggunakan piksel dalam daerah yang didefinisikan oleh 𝑆𝑥𝑦. Tipe noise yang digunakan dalam penelitian ini adalah gaussian noise. Gaussian noise memiliki intensitas yang sesuai dengan distribusi normal yang memiliki rerata (mean) dan varian tertentu. Penelitian ini menguji seberapa efektif metode harmonic mean filter dalam mereduksi gaussian noise menggunakan citra rontgen. penelitian ini menggunakan matriks 5 x 5 dalam sampel perhitungannya. Hasilnya yaitu berupa angka dari hasil metode perhitungan harmonic mean filter dalam bentuk tabel.Kata kunci: Harmonic Mean Filter, Citra Digital, Gaussian Noise, Reduksi, Rontgen. Abstract - In this research, noise is generated using MATLAB tools and analyzed. Therefore, to reduce noise, it is necessary to carry out an image quality improvement process for images that experience noise or noise so that the image can be easily interpreted by both humans and machines. This study uses the Harmonic Mean Filter method. Harmonic Mean Filter is one method to calculate the average of the damaged image g(s,t) in a block image area defined by . The value of the fixed f(x,y) image at each point (x,y) is calculated using only pixels in the area defined by . The type of noise used in this study is Gaussian Noise. Gaussian Noise has an intensity that corresponds to a normal distribution which has a certain mean (mean) and variance. This study examines how effective the Harmonic Mean Filter method is in reducing Gaussian Noise using X-ray images. This study uses a 5 x 5 matrix in the calculation sample. The result is a number from the results of the Harmonic mean filter calculation method in tabular form.Keywords: Harmonic Mean Filter, Digital Image, Gaussian Noise, Reduction, X-Ray.

Penerapan Metode Harmonic Mean Filter Dalam Mereduksi Gaussian Noise Pada Citra Digital

Data processing to produce a computerized information is a tool that needed at this time in various types of business because the information capable presented within quickly and accurately time. Information which capable of quickly and accurately presented can produce quickly and effective decision but the database design that applied in a database management system is no less important Good database design will facilitate the implementation of aplications and optimize the performance of the database manajemen system itself.

http://www.e-journal.stmikkomputama.ac.id/index.php/jtb/article/download/16/8

Perancangan Basis Data Sistem Informasi Penjualan Barang (Studi Kasus: Minimarket “ABC” Sidareja)

The development of Private Universities from year to year is growing. This is directly proportional to the development of student organizations and student activity units. One of the private universities is STMIK Komputama Majenang. STMIK Komputama consists of several Student Activity Units and Student Organizations. STMIK Komputama also has 3 campuses in which each campus has several Student Activity Units and the same Student Organization. With the existence of these 3 campus units, STMIK Komputama Majenang has difficulty in terms of coordination and also the synchronization of the Activities Agenda of the Student Activity Units and Student Organizations. The Enterprise Data Management Architecture is used to connect 3 campuses belonging to STMIK Komputama Majenang. Enterprise Data Management Architecture is the application of architecture used to carry out data management in a Student Activity Unit and Student Organization. In this research, an architecture that is able to synchronize and also produce a good planning of activities in 3 campus of STMIK Komputama Majenang

http://e-journal.stmikkomputama.ac.id/index.php/jtb/article/download/15/9

Arsitektur Enterprise Data Management Untuk Unit Kegiatan Mahasiswa & Organisasi Mahasiswa di STMIK Komputama Majenang





Kemajuan akademi besar yang terdapat di indonesia bertumbuh amat kilat. Paling utama akademi besar swasta yang tiap tahunnya bertambah. Dengan banyaknya akademi besar swasta tiap tahunnya. Perihal ini yang menghasilkan tiap akademi besar swasta bersaingan buat menarik paling- paling pendapatan mahasiswa terkini. Dengan banyak pola metode apalagi menawarkan sebagian program beasiswa pada calon mahasiswa terkini spesialnya di STMIK. Sering- kali akademi besar swasta melaksanakan banyak advertensi bagus itu alat cap ataupun alat elektronik buat menarik atensi calon mahasiswa terkini. Dengan melaksanakan banyak advertensi dimedia bagus itu cap ataupun elektronik kadangkala hadapi hambatan yang amat penting. Perihal patokan mahasiswa yang hendak mencatat ataupun wilayah mana yang mempunyai atensi pada prodi yang dipromosikan. Buat itu butuh terdapatnya database yang melihatkan wilayah ataupun asal bidang yang terpikat ke prodi spesialnya prodi yang terdapat di STMIK. Database ataupun aplikasi ini bisa menunjukkan informasi berhubungan dengan pendapatan mahasiswa terkini yang telah dicoba oleh STMIK. Tata cara Fp Tree Serta Frequent Pattern Growth ialah tata cara yang bisa dipakai buat mengenali gabungan ataupun federasi yang kerap timbul pada database. Frequent pattern growth mengenakan pendekatan yang berlainan dari paradigma yang dipakai pada algoritma yang lain. Frequent pattern tree yakni wujud penyimpanandata yang dimampatkan. Frequent pattern tree dibangun dengan menggambarkan masing- masing data bidang usaha ke dalam masing- masing jalur spesial dalam frequent pattern tree. Hasil riset membuktikan 25 item muadzin pada prodi sistem data a. dengan minimal support 15%. Dan membuktikan 17 item ipa pada prodi sistem data a dengan minimal confidence 50%.





Fajar Mahardika

Papers

Implementasi Metode Waterfall pada Proses Digitalisasi Citra Analog

Implementation Segmentation of Color Image with Detection of Color to Detect Object

Perancangan Basis Data Sistem Informasi Penjualan Barang (Studi Kasus: Minimarket “ABC” Sidareja)

Arsitektur Enterprise Data Management Untuk Unit Kegiatan Mahasiswa & Organisasi Mahasiswa di STMIK Komputama Majenang

Penerapan Metode FP Tree dan Frequent Pattern Growth pada Penerimaan Mahasiswa Baru STMIK