Color Constancy Adjustment using Sub-blocks of the Image

Extreme presence of the source light in digital images decreases the performance of many image processing algorithms, such as video analytics, object tracking and image segmentation. This paper presents a color constancy adjustment technique, which lessens the impact of large unvarying color areas of the image on the performance of the existing statistical based color correction algorithms. The proposed algorithm splits the input image into several non-overlapping blocks. It uses the Average Absolute Difference (AAD) value of each block’s color component as a measure to determine if the block has adequate color information to contribute to the color adjustment of the whole image. It is shown through experiments that by excluding the unvarying color areas of the image, the performances of the existing statistical-based color constancy methods are significantly improved. The experimental results of four benchmark image datasets validate that the proposed framework using Gray World, Max-RGB and Shades of Gray statistics-based methods’ images have significantly higher subjective and competitive objective color constancy than those of the existing and the state-of-the-art methods’ images

Physics-Based Visual Inference: Theory and Applications

Analyzing images to infer physical scene properties is a fundamental task in computer vision. It is by nature an ill-posed inverse problem, because imaging is a complicated, information-lossy physical and measurement process that cannot be deterministically inverted. This dissertation presents theory and algorithms for handling ambiguities in a variety of low-level vision problems. They are based on two key ideas: (1) explicitly modeling and reporting uncertainties are beneficial to visual inference; and (2) using local models can significantly reduce ambiguities that would exist in pixelwise analysis. In the first part of the dissertation, we study the color measurement pipeline of consumer digital cameras, and consider the inherent uncertainty of undoing the effects of tone-mapping. We introduce statistical models for this uncertainty and algorithms for fitting it to given cameras or imaging pipelines. Once fit, the model provides for each tone-mapped color a probability distribution over linear scene colors that could have induced it, which is demonstrated to be useful for a number of downstream inference applications. In the second part of the dissertation, we study the pixelwise ambiguities in physics-based visual inference and present theory and algorithms for employing local models to eliminate or reduce these ambiguities. In shape from shading, we perform mathematical analysis showing that when restricted with quadratic local models, the shape and lighting ambiguities will be reduced to a small finite number of choices as opposed to otherwise continuous manifolds. We propose a framework for surface reconstruction by enforcing consensus on the local regions, which is later enhanced and extended to be applicable to a variety of other visual inference tasks.Engineering and Applied Sciences - Engineering Science

Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Development of Correspondence Field and Its Application to Effective Depth Estimation in Stereo Camera Systems

Stereo camera systems are still the most widely used apparatus for estimating 3D or depth information of a scene due to their low-cost. Estimation of depth using a stereo camera requires first estimating the disparity map using stereo matching algorithms and calculating depth via triangulation based on the camera arrangement (their locations and orientations with respect to the scene). In almost all cases, the arrangement is determined based on human experience since there lacks an effective theoretical tool to guide the design of the camera arrangement. This thesis presents the development of a novel tool, called correspondence field (CF), and its application to optimize the stereo camera arrangement for depth estimation

Intrinsic Images and their Applications in Intelligent Systems

The overall goal of the thesis is to research intelligent systems and to provide one more innovative piece in the puzzle towards general artificial intelligence. Because one quickly realizes the importance of computer vision for this endeavor, and in there specifically the need to understand the 3D world through their 2D projections into images, we thoroughly investigate the field of intrinsic images in this thesis and improve the intrinsic decomposition of arbitrary images to enable smarter intelligent systems. We demonstrate the utilization of such a decomposition in the task of relighting, where the intrinsic structure is shown to improve results

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity