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Статті в журналах з теми "080106 Image Processing"
Fadaeian, Aida, Akram Esvand Rahmani, and Reza Javid. "Classification of Melanoma Images Using Empirical Wavelet Transform." Review of Computer Engineering Studies 8, no. 1 (March 31, 2021): 1–8. http://dx.doi.org/10.18280/rces.080101.
Повний текст джерелаHussien, Amar Y. "Image Steganography Based Spatial and Transform Domain Techniques: A Review." Fusion: Practice and Applications 8, no. 1 (2022): 08–15. http://dx.doi.org/10.54216/fpa.080101.
Повний текст джерелаДисертації з теми "080106 Image Processing"
Kerwin, Matthew. "Comparison of Traditional Image Segmentation Techniques and Geostatistical Threshold." Thesis, James Cook University, 2006. https://eprints.qut.edu.au/99764/1/kerwin-honours-thesis.pdf.
Повний текст джерелаPeynot, Thierry. "Selection et controle de modes de deplacement pour un robot mobile autonome en environnements naturels." Thesis, Institut National Polytechnique de Toulouse, 2006. http://ethesis.inp-toulouse.fr/archive/00000395/.
Повний текст джерелаLeitner, Jürgen. "From vision to actions: Towards adaptive and autonomous humanoid robots." Thesis, Università della Svizzera Italiana, 2014. https://eprints.qut.edu.au/90178/2/2014INFO020.pdf.
Повний текст джерелаChen, Fang. "Facial Feature Point Detection." Thesis, 2011. http://hdl.handle.net/1807/30546.
Повний текст джерелаLenc, Emil. "Digital image transformation and compression." Thesis, 1996. https://vuir.vu.edu.au/17915/.
Повний текст джерелаGanin, Iaroslav. "Natural image processing and synthesis using deep learning." Thèse, 2019. http://hdl.handle.net/1866/23437.
Повний текст джерелаIn the present thesis, we study how deep neural networks can be applied to various tasks in computer vision. Computer vision is an interdisciplinary field that deals with understanding of digital images and video. Traditionally, the problems arising in this domain were tackled using heavily hand-engineered adhoc methods. A typical computer vision system up until recently consisted of a sequence of independent modules which barely talked to each other. Such an approach is quite reasonable in the case of limited data as it takes major advantage of the researcher's domain expertise. This strength turns into a weakness if some of the input scenarios are overlooked in the algorithm design process. With the rapidly increasing volumes and varieties of data and the advent of cheaper and faster computational resources end-to-end deep neural networks have become an appealing alternative to the traditional computer vision pipelines. We demonstrate this in a series of research articles, each of which considers a particular task of either image analysis or synthesis and presenting a solution based on a ``deep'' backbone. In the first article, we deal with a classic low-level vision problem of edge detection. Inspired by a top-performing non-neural approach, we take a step towards building an end-to-end system by combining feature extraction and description in a single convolutional network. The resulting fully data-driven method matches or surpasses the detection quality of the existing conventional approaches in the settings for which they were designed while being significantly more usable in the out-of-domain situations. In our second article, we introduce a custom architecture for image manipulation based on the idea that most of the pixels in the output image can be directly copied from the input. This technique bears several significant advantages over the naive black-box neural approach. It retains the level of detail of the original images, does not introduce artifacts due to insufficient capacity of the underlying neural network and simplifies training process, to name a few. We demonstrate the efficiency of the proposed architecture on the challenging gaze correction task where our system achieves excellent results. In the third article, we slightly diverge from pure computer vision and study a more general problem of domain adaption. There, we introduce a novel training-time algorithm (\ie, adaptation is attained by using an auxilliary objective in addition to the main one). We seek to extract features that maximally confuse a dedicated network called domain classifier while being useful for the task at hand. The domain classifier is learned simultaneosly with the features and attempts to tell whether those features are coming from the source or the target domain. The proposed technique is easy to implement, yet results in superior performance in all the standard benchmarks. Finally, the fourth article presents a new kind of generative model for image data. Unlike conventional neural network based approaches our system dubbed SPIRAL describes images in terms of concise low-level programs executed by off-the-shelf rendering software used by humans to create visual content. Among other things, this allows SPIRAL not to waste its capacity on minutae of datasets and focus more on the global structure. The latent space of our model is easily interpretable by design and provides means for predictable image manipulation. We test our approach on several popular datasets and demonstrate its power and flexibility.
Tse, Kwok Chung. "Efficient storage and retrieval methods for multimedia information." Thesis, 1999. https://vuir.vu.edu.au/15370/.
Повний текст джерелаShen, Zhenliang. "Colour differentiation in digitial images." Thesis, 2003. https://vuir.vu.edu.au/15529/.
Повний текст джерелаYan, Shuo. "Adaptive Image Quality Improvement with Bayesian Classification for In-line Monitoring." Thesis, 2008. http://hdl.handle.net/1807/11279.
Повний текст джерелаAzzam, Ibrahim Ahmed Aref. "Implicit Concept-based Image Indexing and Retrieval for Visual Information Systems." Thesis, 2006. https://vuir.vu.edu.au/479/.
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