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"This book continues first one of the same authors Adaptive Image Processing Algorithms for Printing and presents methods and software solutions for copying and scanning various types of documents by conventional office equipment, offering techniques for correction of distortions and enhancement of scanned documents; techniques for automatic cropping and de-skew; approaches for segmentation of text and picture regions; documents classifiers; approach for vectorization of symbols by approximation of their contour by curves; methods for optimal compression of scanned documents, algorithm for stitching parts of large originals; copy-protection methods by microprinting and embedding of hidden information to hardcopy; algorithmic approach for toner saving. In addition, method for integral printing is considered. Described techniques operate in automatic mode thanks to machine learning or ingenious heuristics. Most the techniques presented have a low computational complexity and memory consumption due to they were designed for firmware of embedded systems or software drivers. The book reflects the authors practical experience in algorithm development for industrial R&D. "

"This book offers a comprehensive introduction to advanced methods for image and video analysis and processing. It covers deraining, dehazing, inpainting, fusion, watermarking and stitching. It describes techniques for face and lip recognition, facial expression recognition, lip reading in videos, moving object tracking, dynamic scene classification, among others.The book combines the latest machine learning methods with computer vision applications, covering topics such as event recognition based on deep learning，dynamic scene classification based on topic model, person re-identification based on metric learning and behavior analysis. It also offers a systematic introduction to image evaluation criteria showing how to use them in different experimental contexts.The book offers an example-based practical guide to researchers, professionals and graduate students dealing with advanced problems in image analysis and computer vision."

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Pengembangan algoritma untuk kendali quadrotor semakin masif dilakukan oleh peneliti diseluruh dunia. Sama seperti manusia yang melihat dan kemudian dapat mendekati dan menyentuh suatu obyek, penelitian ini juga diarahkan untuk menciptakan prinsip yang sama yang kami sebut sebagai Image Loop Control (ILC). Proses pendeteksian objek memanfaatkan kecerdasan buatan YOLOv8 (AI deep learning) sebagai state-of-the-art pada dunia pendeteksian objek kecil membawa performa pendeteksian objek kecil ke tingkat yang lebih tinggi dengan inovasinya yang revolusioner. Penerapannya di quadrotor diharapkan dapat memungkinkan tingkat otonomi pada otomasi quadrotor melalui image loop control tersebut. Di dalam ILC tetap digunakan kendali Proporsional dan Differensial (PD) untuk mengendalikan gerak pada tiap sumbu gerakan. Skripsi ini melaporkan gerak yaw yang dilakukan oleh quadrotor sebagai respon dari deteksi obyek oleh YOLOv8. Pada proses validasi hasil pelatihan dataset, sebesar 96% gambar pintu tertutup terdeteksi sebagai close, 94% gambar pintu terbuka terdeteksi sebagai open, dan 87% gambar pintu setengah terbuka terdeteksi sebagai semi. Hasil proses image loop control respon kontroler PD di sumbu yaw, memiliki rata-rata time delay sebesar 0,98 detik, rata-rata rise time sebesar 1,26 detik, dan rata-rata settling time sebesar 8,62 detik menggunakan nilai Kp = 1,2 dan Kd = 0,5.

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The development of quadrotor control algorithm has been extensively pursued by numerous researchers around the world. Similar to how humans can look, move around, and interact with an object, this research aims to achieve the same through a principle we define as the Image Loop Control (ILC). The process of object detection using the artificial intelligence YOLOv8 (deep learning AI) as the state-of-the-art in the small object detection world has brought the performance of small object detection algorithms to a higher level thanks to its revolutionary innovation. Its implementation in a quadrotor may enhance the degree of autonomy on automated quadrotors by using an image loop control. Within the ILC framework, we use a Proportional and Differential (PD) controller to control quadrotor movements along each axis. This thesis presents the performance of yawing movements executed by the quadrotor in response to object detections identified by the YOLOv8. During the validation process of the trained dataset, the system detected 96% of closed doors accurately, 94% of open doors accurately, and 87% of semi opened doors accurately. The response of the image loop control response using a PD controller on the yaw axis resulted in an average time delay of 0.98 seconds, average rise time of 1.26 seconds, and average settling time of 8.62 seconds with the values Kp = 1.2 and Kd = 0.5."

"This book includes a selection of reviewed papers presented at the 9th China Academic Conference on Printing and Packaging, which was held in November 2018 in Shandong, China. The conference was jointly organized by the China Academy of Printing Technology and Qilu University of Technology (Shandong Academy of Sciences). With 8 keynote talks and over 200 presented papers on graphic communication and packaging technologies, the conference attracted more than 300 scientists.The proceedings cover the recent findings in color science and technology, image processing technology, digital media technology, mechanical engineering and numerical control, materials and detection, digital process management technology in printing and packaging, and other technologies. As such, the book is of interest to university researchers, R&D engineers and graduate students in the field of graphic arts, packaging, color science, image science, material science, computer science, digital media, and network technology."