Colour-based image retrieval algorithms based on compact colour descriptors and dominant colour-based indexing methods

Content based image retrieval (CBIR) is reported as one of the most active research areas in the last two decades, but it is still young. Three CBIR’s performance problem in this study is inaccuracy of image retrieval, high complexity of feature extraction, and degradation of image retrieval after database indexing. This situation led to discrepancies to be applied on limited-resources devices (such as mobile devices). Therefore, the main objective of this thesis is to improve performance of CBIR. Images’ Dominant Colours (DCs) is selected as the key contributor for this purpose due to its compact property and its compatibility with the human visual system. Semantic image retrieval is proposed to solve retrieval inaccuracy problem by concentrating on the images’ objects. The effect of image background is reduced to provide more focus on the object by setting weights to the object and the background DCs. The accuracy improvement ratio is raised up to 50% over the compared methods. Weighting DCs framework is proposed to generalize this technique where it is demonstrated by applying it on many colour descriptors. For reducing high complexity of colour Correlogram in terms of computations and memory space, compact representation of Correlogram is proposed. Additionally, similarity measure of an existing DC-based Correlogram is adapted to improve its accuracy. Both methods are incorporated to produce promising colour descriptor in terms of time and memory space complexity. As a result, the accuracy is increased up to 30% over the existing methods and the memory space is decreased to less than 10% of its original space. Converting the abundance of colours into a few DCs framework is proposed to generalize DCs concept. In addition, two DC-based indexing techniques are proposed to overcome time problem, by using RGB and perceptual LUV colour spaces. Both methods reduce the search space to less than 25% of the database size with preserving the same accuracy

VToonify: Controllable High-Resolution Portrait Video Style Transfer

Generating high-quality artistic portrait videos is an important and desirable task in computer graphics and vision. Although a series of successful portrait image toonification models built upon the powerful StyleGAN have been proposed, these image-oriented methods have obvious limitations when applied to videos, such as the fixed frame size, the requirement of face alignment, missing non-facial details and temporal inconsistency. In this work, we investigate the challenging controllable high-resolution portrait video style transfer by introducing a novel VToonify framework. Specifically, VToonify leverages the mid- and high-resolution layers of StyleGAN to render high-quality artistic portraits based on the multi-scale content features extracted by an encoder to better preserve the frame details. The resulting fully convolutional architecture accepts non-aligned faces in videos of variable size as input, contributing to complete face regions with natural motions in the output. Our framework is compatible with existing StyleGAN-based image toonification models to extend them to video toonification, and inherits appealing features of these models for flexible style control on color and intensity. This work presents two instantiations of VToonify built upon Toonify and DualStyleGAN for collection-based and exemplar-based portrait video style transfer, respectively. Extensive experimental results demonstrate the effectiveness of our proposed VToonify framework over existing methods in generating high-quality and temporally-coherent artistic portrait videos with flexible style controls.Comment: ACM Transactions on Graphics (SIGGRAPH Asia 2022). Code: https://github.com/williamyang1991/VToonify Project page: https://www.mmlab-ntu.com/project/vtoonify

Motion enriching using humanoide captured motions

Animated humanoid characters are a delight to watch. Nowadays they are extensively used in simulators. In military applications animated characters are used for training soldiers, in medical they are used for studying to detect the problems in the joints of a patient, moreover they can be used for instructing people for an event(such as weather forecasts or giving a lecture in virtual environment). In addition to these environments computer games and 3D animation movies are taking the benefit of animated characters to be more realistic. For all of these mediums motion capture data has a great impact because of its speed and robustness and the ability to capture various motions. Motion capture method can be reused to blend various motion styles. Furthermore we can generate more motions from a single motion data by processing each joint data individually if a motion is cyclic. If the motion is cyclic it is highly probable that each joint is defined by combinations of different signals. On the other hand, irrespective of method selected, creating animation by hand is a time consuming and costly process for people who are working in the art side. For these reasons we can use the databases which are open to everyone such as Computer Graphics Laboratory of Carnegie Mellon University.Creating a new motion from scratch by hand by using some spatial tools (such as 3DS Max, Maya, Natural Motion Endorphin or Blender) or by reusing motion captured data has some difficulties. Irrespective of the motion type selected to be animated (cartoonish, caricaturist or very realistic) human beings are natural experts on any kind of motion. Since we are experienced with other peoples’ motions, and comparing each motion to the others, we can easily judge one individual’s mood from his/her body language. As being a natural master of human motions it is very difficult to convince people by a humanoid character’s animation since the recreated motions can include some unnatural artifacts (such as foot-skating, flickering of a joint)