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"This book is suitable for independent study, reference, or coursework at the university level and beyond for experienced engineers and scientists. The chapters are divided in such a way that various courses can be devised to incorporate a subset of chapters to accommodate course requirements. For example, typical course titles include “Image Sensors and Image Processing,” “Computer Vision And Image Processing,” “Applied Computer Vision And Imaging Optimizations,” “Feature Learning, Deep Learning, and Neural Network Architectures,” “Computer Vision Architectures,” “Computer Vision Survey.” Questions are available for coursework at theend of each chapter. It is recommended that this book be used as a omplement to other fine books, open source code, and hands-on materials for study in computer vision and related scientific disciplines, or possibly used by itself for a higher-level survey course."

"This book constitutes the thoroughly refereed post-conference proceedings of the Third International Conference on Scale Space Methods and Variational Methods in Computer Vision, SSVM 2011, held in Ein-Gedi, Israel in May/June 2011. The 24 revised full papers presented together with 44 poster papers were carefully reviewed and selected from 78 submissions. The papers are organized in topical sections on denoising and enhancement, segmentation, image representation and invariants, shape analysis, and optical flow."

"Salah satu kemampuan robot yang canggih adalah mampu melakukan adaptasi pada lingkungan sekitarnya. Kemampuan ini merupakan analogi terhadap kemampuan manusia secara khusus. Namun, kebanyakan robot yang dibuat masih terbatas dalam hal interaksi secara sentuhan dengan lingkungan sekitarnya. Oleh karenanya diperlukan sistem sensasi non-kontak yang salah satunya adalah sensasi secara visual. Cara ini termasuk salah satu yang paling advance karena hampir semua proses manipulasi bisa dilakukan dengan hanya menggunakan sensor visual yaitu kamera walaupun computational cost-nya cukup tinggi. Single Board computer jenis BeagleBoard akan digunakan untuk melakukan komputasi sensasi visual yang meliputi face detection, stereo vision, dan bahkan lokalisasi nantinya. Wajah manusia yang akan dikenali oleh sistem computer visualnya akan di-tracking dan diukur jaraknya secara real time melalui teknik stereo vision. Koordinat yang didapat akan ditransformasikan dengan persamaan kinematik berupa invers jacobian menuju pusat robot untuk melakukan aktuasi pada aktuator vision dan navigasi robot secara keseluruhan sampai tujuan untuk melakukan interaksi dengan manusia tercapai. Berdasarkan pengujian yang telah dilakukan dapat dinyatakan bahwa sistem komputer vision yang telah dibangun cukup valid dan handal untuk jarak dibawah 100 cm walaupun dengan waktu komputasi yang cukup besar.

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One of the advance robot's ability is it can adapt into the around environment. This ability itself is the analogy of human's. But now, most of the robots still have limited in contact sensation. So, it's needed to build non-contact sensations and one of them is reached by build visual system. This way belong to one of advance method because almost of manipulation way can be dealed with this visual sensor like camera, even though the computational cost is high enough.

BeagleBoard, a kind of powerful Single Board computer, will be use to compute the visual sensation in this receptionist robot include face detection, stereo vision, and even localization later. The face of human that will be recognized by visual computer system will be tracked and the distant is calculated real time via stereo vision system. The coordinate that has been gathered will be transformed by invers jacobian into the center of robot to actuate visual actuation and doing robot navigation until receptionist robot is able to do interaction with human. Based on the result of experiment, it can be stated that the developed computer vision system is valid and reliable enough for distant below 100 cm even though spends high computational time."

"Computer vision has widespread and growing application including robotics, autonomous vehicles, medical imaging and diagnosis, surveillance, video analysis, and even tracking for sports analysis. This book equips the reader with crucial mathematical and algorithmic tools to develop a thorough understanding of the underlying components of any complete computer vision system and to design such systems. These components include identifying local features such as corners or edges in the presence of noise, edge preserving smoothing, connected component labeling, stereopsis, thresholding, clustering, segmentation, and describing and matching both shapes and scenes. The extensive examples include photographs of faces, cartoons, animal footprints, and angiograms, and each chapter concludes with homework exercises and suggested projects. Intended for advanced undergraduate and beginning graduate students, the text will also be of use to practitioners and researchers in a range of applications."

"Konsumsi rokok menjadi salah satu isu kesehatan global terbesar di dunia. Organisasi Kesehatan Dunia (WHO) memperkirakan sekitar 1,3 miliar penduduk di seluruh dunia menggunakan produk tembakau. Indonesia juga menempati peringkat ketiga tertinggi jumlah perokok aktif terbanyak di dunia. Tembakau tidak hanya berbahaya bagi yang menggunakannya, tetapi juga berbahaya bagi yang terpapar asapnya. Orang dapat merokok sembarangan dengan mudah jika pengawasan terhadap penggunaan rokok longgar atau bahkan tidak ditegakkan. Untuk mengatasi permasalahan rokok, berbagai penelitian telah dikembangkan, termasuk metode pengenalan orang yang sedang merokok. Berbagai perangkat pencitraan digunakan untuk mendeteksi aktivitas manusia, termasuk merokok. Dengan pesatnya perkembangan kecerdasan buatan dan deep learning dalam beberapa dekade terakhir, termasuk computer vision, berbagai metode telah dikembangkan untuk mendeteksi orang yang sedang merokok. Salah satu metode tersebut adalah MobileNetV3, yang merupakan salah satu arsitektur Convolutional Neural Network (CNN). MobileNetV3 dikembangkan khusus untuk penggunaan pada aplikasi peranti bergerak dan sistem tanam karena sifatnya yang ringan komputasi. Penelitian ini bertujuan untuk mengembangkan sistem deteksi orang sedang merokok berbasis computer vision menggunakan MobileNetV3. Pada arsitektur sistem, layer dropout digunakan untuk mengatasi masalah overfitting sehingga performa model meningkat. Dataset yang digunakan berasal dari Mendeley Data dan Kaggle yang merupakan kumpulan citra orang yang sedang merokok masing-masing sejumlah 2410 citra dan 3275 citra. Melalui simulasi menggunakan konfigurasi dropout senilai 0,5, perbandingan proporsi dataset training : validasi : training menjadi 80 : 10 : 10, model berhasil memperoleh performa terbaik dengan nilai akurasi sebesar 92,08%, nilai loss sebesar 22,87%, nilai presisi sebesar 93,16%, dan nilai recall sebesar 90,83%. Akurasi ini lebih baik dari penelitian Junlong Tang et al. dengan YOLOv5s yang menghasilkan akurasi 85,6%

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Cigarette consumption is one of the most significant global health issues. The World Health Organization (WHO) estimates that around 1.3 billion people worldwide use tobacco products. Indonesia also ranks third with the world's highest number of active smokers. Tobacco is not only dangerous for those who use it but also for those exposed to the smoke. People can smoke indiscriminately if controls on cigarette use are lax or even not enforced. Various studies have been developed to overcome the problem of smoking, including methods of identifying people who smoke. Different imaging devices are used to detect human activities, including smoking behavior. With the rapid development of artificial intelligence and deep learning in recent decades, including computer vision, various methods have been developed to detect smoking people. One such method is MobileNetV3, one of the Convolutional Neural Network (CNN) architectures. MobileNetV3 was explicitly developed for mobile applications and embedded systems because of its computationally lightweight nature. This study aims to create a computer vision-based smoking detection system using MobileNetV3. In the system architecture, the dropout layer is used to overcome the problem of overfitting so that model performance increases. The datasets used are from Mendeley Data and Kaggle, a collection of images of smoking people, a total of 2410 and 3275 images, respectively. Through simulation using a dropout configuration of 0.5, the proportion of the training dataset: validation: training to 80: 10: 10, the model managed to obtain the best performance with an accuracy value of 92.08%, a loss value of 22.87%, a precision value of 93.16%, and the recall value is 90.83%. This accuracy is better than previous studies by Junlong Tang et al. with YOLOv5s, which resulted in an accuracy of 85.6%"

"Latar belakang: Penggunaan komputer dapat menimbulkan suatu keluhan kesehatan yang disebut dengan Computer Vision Syndrome (CVS), Sindrom ini dapat dipengaruhi oleh berbagai faktor risiko individual, lingkungan dan komputer. Tujuan: Mengidentifikasi dan menganalisis faktor-faktor resiko ergonomik individual dan komputer yang berhubungan dengan kejadian Computer Vision Syndrome (CVS) pada pekerja pengguna komputer yang berkacamata dan pekerja yang tidak berkacamata. Metode: Penelitian ini merupakan penelitian metode kualitatatif. Penelitian dilakukan pada bulan April - Mei 2013 di Unit Pelakasana dan Pelatihan. Sampel sebanyak 18 orang dengan kriteria tertentu, dibagi menjadi 2 kelompok pekerja berkacamata dan pekerja yang tidak berkacamata. Peneliltian dilakukan dengan wawancara langsung menggunakan kuesioner dan pengukuran. Hasil: Faktor-faktor yang berhubungan dengan kejadian CVS adalah Kelembaban 71%, Pencahayaan kurang dari 300-500 lux (KEPMENKES nomor 1405/Menkes/SK/XI/2002), Usia lebih dari 40 tahun (das et al.), lama bekerja dengan komputer, dan jarak komputer dengan mata. Kesimpulan: Gejala ekstraokuler pada pekerja pengguna kacamata bifocal melakukan retrofleksi leher sehingga leher tertekuk kebelakang yang menyebabkan keluhan nyeri pada leher. Penderita terbanyak bukan dari pengguna kacamata tetapi pada pekerja yang tidak berkacamata. Serta penderita CVS (berdasarkan kriteria anamnesa) di usia 25 tahun, kedua hal ini berkaitan dengan potur ergonomi pada saat kerja baik secara design tempat kerja, kondisi ruangan ataupun durasi kerja yang semuanya saling berkaitan sehingga menimbulkan gejala Computer Vision Syndrome (CVS).

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Background: Computer usage could cause health complaints called Computer Vision Syndrome (CVS). This syndrome was influenced by individual and computer risk factors.

Aim: The objective of the study is to identify and to analyze individual and computer factors of computer Vision Syndrome (CVS).

Methods: This study was an observational study with methods qualitatively. The research was conducted in April-May 2013 in the Pelakasana and Training Unit. Sample of 18 people with certain criteria, divided into 2 groups of workers and workers who are not wearing glasses glasses. Peneliltian done by direct interviews using questionnaires and measurements.

Results: Factors associated with the incidence of CVS is Humidity 71%, less than the 300-500 lux lighting (KEPMENKES 1405/Menkes/SK/XI/2002), age over 40 years (das et al.), Long working computers, and computer distance by eye.

Conclusion: Extraocular symptoms in workers bifocal glasses users do retrofleksi neck so the neck is bent backwards which causes pain in the neck. Most patients but not from users goggles to workers who do not wear glasses. And people with CVS (based on criteria anamnesis) at the age of 25 years, these two things related to ergonomic posture at work both in design work, ambient conditions or duration of action that are all intertwined, giving rise to symptoms of Computer Vision Syndrome (CVS)."