1,234 research outputs found

    Perceptually Inspired Real-time Artistic Style Transfer for Video Stream

    Get PDF
    This study presents a real-time texture transfer method for artistic style transfer for video stream. We propose a parallel framework using a T-shaped kernel to enhance the computational performance. With regard to accelerated motion estimation, which is necessarily required for maintaining temporal coherence, we present a method using a downscaled motion field to successfully achieve high real-time performance for texture transfer of video stream. In addition, to enhance the artistic quality, we calculate the level of abstraction using visual saliency and integrate it with the texture transfer algorithm. Thus, our algorithm can stylize video with perceptual enhancements

    Two-Stream Convolutional Networks for Dynamic Texture Synthesis

    Get PDF
    This thesis introduces a two-stream model for dynamic texture synthesis. The model is based on pre-trained convolutional networks (ConvNets) that target two independent tasks: (i) object recognition, and (ii) optical flow regression. Given an input dynamic texture, statistics of filter responses from the object recognition and optical flow ConvNets encapsulate the per-frame appearance and dynamics of the input texture, respectively. To synthesize a dynamic texture, a randomly initialized input sequence is optimized to match the feature statistics from each stream of an example texture. In addition, the synthesis approach is applied to combine the texture appearance from one texture with the dynamics of another to generate entirely novel dynamic textures. Overall, the proposed approach generates high quality samples that match both the framewise appearance and temporal evolution of input texture. Finally, a quantitative evaluation of the proposed dynamic texture synthesis approach is performed via a large-scale user study

    Diversified Texture Synthesis with Feed-forward Networks

    Full text link
    Recent progresses on deep discriminative and generative modeling have shown promising results on texture synthesis. However, existing feed-forward based methods trade off generality for efficiency, which suffer from many issues, such as shortage of generality (i.e., build one network per texture), lack of diversity (i.e., always produce visually identical output) and suboptimality (i.e., generate less satisfying visual effects). In this work, we focus on solving these issues for improved texture synthesis. We propose a deep generative feed-forward network which enables efficient synthesis of multiple textures within one single network and meaningful interpolation between them. Meanwhile, a suite of important techniques are introduced to achieve better convergence and diversity. With extensive experiments, we demonstrate the effectiveness of the proposed model and techniques for synthesizing a large number of textures and show its applications with the stylization.Comment: accepted by CVPR201

    A New Automatic Watercolour Painting Algorithm Based on Dual Stream Image Segmentation Model with Colour Space Estimation

    Get PDF
    Image processing plays a crucial role in automatic watercolor painting by manipulating the digital image to achieve the desired watercolor effect. segmentation in automatic watercolor painting algorithms is essential for region-based processing, color mixing and blending, capturing brushwork and texture, and providing artistic control over the final result. It allows for more realistic and expressive watercolor-like paintings by processing different image regions individually and applying appropriate effects to each segment. Hence, this paper proposed an effective Dual Stream Exception Maximization (DSEM) for automatic image segmentation. DSEM combines both color and texture information to segment an image into meaningful regions. This approach begins by converting the image from the RGB color space to a perceptually-based color space, such as CIELAB, to account for variations in lighting conditions and human perception of color.  With the color space conversion, DSEM extracts relevant features from the image. Color features are computed based on the values of the color channels in the chosen color space, capturing the nuances of color distribution within the image. Simultaneously, texture features are derived by computing statistical measures such as local variance or co-occurrence matrices, capturing the textural characteristics of the image. Finally, the model is applied over the deep learning model for the classification of the color space in the painting. Simulation analysis is performed compared with conventional segmentation techniques such a CNN and RNN. The comparative analysis states that the proposed DSEM exhibits superior performance compared to conventional techniques in terms of color space estimation, texture analysis and region merging. The performance of classification with DSEM is ~12% higher than the conventional techniques

    Empiricism without Magic: Transformational Abstraction in Deep Convolutional Neural Networks

    Get PDF
    In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain

    A Temporally Coherent Neural Algorithm for Artistic Style Transfer

    Get PDF
    Within the fields of visual effects and animation, humans have historically spent countless painstaking hours mastering the skill of drawing frame-by-frame animations. One such animation technique that has been widely used in the animation and visual effects industry is called rotoscoping and has allowed uniquely stylized animations to capture the motion of real life action sequences, however it is a very complex and time consuming process. Automating this arduous technique would free animators from performing frame by frame stylization and allow them to concentrate on their own artistic contributions. This thesis introduces a new artificial system based on an existing neural style transfer method which creates artistically stylized animations that simultaneously reproduce both the motion of the original videos that they are derived from and the unique style of a given artistic work. This system utilizes a convolutional neural network framework to extract a hierarchy of image features used for generating images that appear visually similar to a given artistic style while at the same time faithfully preserving temporal content. The use of optical flow allows the combination of style and content to be integrated directly with the apparent motion over frames of a video to produce smooth and visually appealing transitions. The implementation described in this thesis demonstrates how biologically-inspired systems such as convolutional neural networks are rapidly approaching human-level behavior in tasks that were once thought impossible for computers. Such a complex task elucidates the current and future technical and artistic capabilities of such biologically-inspired neural systems as their horizons expand exponentially. Further, this research provides unique insights into the way that humans perceive and utilize temporal information in everyday tasks. A secondary implementation that is explored in this thesis seeks to improve existing convolutional neural networks using a biological approach to the way these models adapt to their inputs. This implementation shows how these pattern recognition systems can be greatly improved by integrating recent neuroscience research into already biologically inspired systems. Such a novel hybrid activation function model replicates recent findings in the field of neuroscience and shows significant advantages over existing static activation functions

    A survey of exemplar-based texture synthesis

    Full text link
    Exemplar-based texture synthesis is the process of generating, from an input sample, new texture images of arbitrary size and which are perceptually equivalent to the sample. The two main approaches are statistics-based methods and patch re-arrangement methods. In the first class, a texture is characterized by a statistical signature; then, a random sampling conditioned to this signature produces genuinely different texture images. The second class boils down to a clever "copy-paste" procedure, which stitches together large regions of the sample. Hybrid methods try to combine ideas from both approaches to avoid their hurdles. The recent approaches using convolutional neural networks fit to this classification, some being statistical and others performing patch re-arrangement in the feature space. They produce impressive synthesis on various kinds of textures. Nevertheless, we found that most real textures are organized at multiple scales, with global structures revealed at coarse scales and highly varying details at finer ones. Thus, when confronted with large natural images of textures the results of state-of-the-art methods degrade rapidly, and the problem of modeling them remains wide open.Comment: v2: Added comments and typos fixes. New section added to describe FRAME. New method presented: CNNMR
    • …
    corecore