IMPELEMENTASI METODE INTERAKSI BERBASIS KAMERA PADA HANDPHONE BERPLATFORM SYMBIAN UNTUK BERAGAM APLIKASI

ABSTRAKSI: Perkembangan metode interaksi pada handphones terus berkembang. Penerapan teknologi berupa tombol, layar sentuh ataupun accelerometer sudah lazim kita temui pada handphone belakangan ini. Kelemahan dari teknologi layar sentuh ataupun accelerometer adalah pada mahalnya perangkat keras tambahan yang harus ditambahkan pada handphone tersebut. Dengan latar belakang tersebut, maka pada Tugas Akhir ini, penulis melakukan implementasi dan analisa metode interaksi dengan menggunakan kamera yang terletak pada handphone. Dengan memanfaatkan kamera handphone sebagai inputan, penulis mencoba untuk membuat sebuah middleware yang berfungsi untuk mengolah gambar, melakukan motion detection lalu mengirimkan key yang telah dihasilkan kepada aplikasi yang berada di foreground. Algoritma yang dipakai adalah Efficient Motion Sensing akan tetapi sedikit dirubah untuk menambah performansinya. Dari hasil pengujian dan analisa yang telah dilakukan dengan menggunakan handphone bertipe Nokia E61i, didapatkan waktu proses algoritma yang telah mengalami perbaikan meningkat dari 687.1ms menjadi 104.2ms dari yang awalnya hanya 1 frame per second menjadi 10 frame per second.Kata Kunci : handphone, metode interaksi, motion detection, perbaikan algoritma, algoritma Efficient MotionABSTRACT: The development of interaction method on handphones are growing rapidly. The application of interaction technology is in the form button, touch screen or accelerometer are commonly be found in the mobile phones lately. The weakness of touch screen technology or accelerometer technology is on the expensive additional hardware that should be added to the mobile phones. With that background, this Final Project, the author try to impelement and analys methods of interaction using camera onboard. By using the mobile phones camera as an input, the author tries to create a middleware that works for image processing, motion detection and send key that has been generated to the application that work in foreground. This middleware using Efficient Motion Sensing Algorithm but slightly altered to increase system performance. After implementation, test and analysis using Nokia E61i for test speciment. Writer can conclude that the enhancement algorithm can run more faster, from 687.1ms to 104.2ms, which initially only 1 frame per second to 10 frame per second.Keyword: handphone, interaction method, motion detection, algorithm enhancement, Efficient Motio

K-Space at TRECVid 2007

In this paper we describe K-Space participation in TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance. The first of the two systems was a ‘shot’ based interface, where the results from a query were presented as a ranked list of shots. The second interface was ‘broadcast’ based, where results were presented as a ranked list of broadcasts. Both systems made use of the outputs of our high-level feature submission as well as low-level visual features

“Are Machines Better Than Humans in Image Tagging?” - A User Study Adds to the Puzzle

“Do machines perform better than humans in visual recognition tasks?” Not so long ago, this question would have been considered even somewhat provoking and the answer would have been clear: “No”. In this paper, we present a comparison of human and machine performance with respect to annotation for multimedia retrieval tasks. Going beyond recent crowdsourcing studies in this respect, we also report results of two extensive user studies. In total, 23 participants were asked to annotate more than 1000 images of a benchmark dataset, which is the most comprehensive study in the field so far. Krippendorff’s α is used to measure inter-coder agreement among several coders and the results are compared with the best machine results. The study is preceded by a summary of studies which compared human and machine performance in different visual and auditory recognition tasks. We discuss the results and derive a methodology in order to compare machine performance in multimedia annotation tasks at human level. This allows us to formally answer the question whether a recognition problem can be considered as solved. Finally, we are going to answer the initial question

“Are Machines Better Than Humans in Image Tagging?” - A User Study Adds to the Puzzle

“Do machines perform better than humans in visual recognition tasks?” Not so long ago, this question would have been considered even somewhat provoking and the answer would have been clear: “No”. In this paper, we present a comparison of human and machine performance with respect to annotation for multimedia retrieval tasks. Going beyond recent crowdsourcing studies in this respect, we also report results of two extensive user studies. In total, 23 participants were asked to annotate more than 1000 images of a benchmark dataset, which is the most comprehensive study in the field so far. Krippendorff’s α is used to measure inter-coder agreement among several coders and the results are compared with the best machine results. The study is preceded by a summary of studies which compared human and machine performance in different visual and auditory recognition tasks. We discuss the results and derive a methodology in order to compare machine performance in multimedia annotation tasks at human level. This allows us to formally answer the question whether a recognition problem can be considered as solved. Finally, we are going to answer the initial question

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