Rejection-Cascade of Gaussians: Real-time adaptive background subtraction framework

Background-Foreground classification is a well-studied problem in computer vision. Due to the pixel-wise nature of modeling and processing in the algorithm, it is usually difficult to satisfy real-time constraints. There is a trade-off between the speed (because of model complexity) and accuracy. Inspired by the rejection cascade of Viola-Jones classifier, we decompose the Gaussian Mixture Model (GMM) into an adaptive cascade of Gaussians(CoG). We achieve a good improvement in speed without compromising the accuracy with respect to the baseline GMM model. We demonstrate a speed-up factor of 4-5x and 17 percent average improvement in accuracy over Wallflowers surveillance datasets. The CoG is then demonstrated to over the latent space representation of images of a convolutional variational autoencoder(VAE). We provide initial results over CDW-2014 dataset, which could speed up background subtraction for deep architectures.Comment: Accepted for National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG 2019

Automated segmentation of tissue images for computerized IHC analysis

This paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologie

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)

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Studies in ambient intelligent lighting

The revolution in lighting we are arguably experiencing is led by technical developments in the area of solid state lighting technology. The improved lifetime, efficiency and environmentally friendly raw materials make LEDs the main contender for the light source of the future. The core of the change is, however, not in the basic technology, but in the way users interact with it and the way the quality of the produced effect on the environment is judged. With the new found freedom the users can switch their focus from the confines of the technology to the expression of their needs, regardless of the details of the lighting system. Identifying the user needs, creating an effective language to communicate them to the system, and translating them to control signals that fulfill them, as well as defining the means to measure the quality of the produced result are the topic of study of a new multidisciplinary area of study, Ambient Intelligent Lighting. This thesis describes a series of studies in the field of Ambient Intelligent Lighting, divided in two parts. The first part of the thesis demonstrates how, by adopting a user centric design philosophy, the traditional control paradigms can be superseded by novel, so-called effect driven controls. Chapter 3 describes an algorithm that, using statistical methods and image processing, generates a set of colors based on a term or set of terms. The algorithm uses Internet image search engines (Google Images, Flickr) to acquire a set of images that represent a term and subsequently extracts representative colors from the set. Additionally, an estimate of the quality of the extracted set of colors is computed. Based on the algorithm, a system that automatically enriches music with lyrics based images and lighting was built and is described. Chapter 4 proposes a novel effect driven control algorithm, enabling users easy, natural and system agnostic means to create a spatial light distribution. By using an emerging technology, visible light communication, and an intuitive effect definition, a real time interactive light design system was developed. Usability studies on a virtual prototype of the system demonstrated the perceived ease of use and increased efficiency of an effect driven approach. In chapter 5, using stochastic models, natural temporal light transitions are modeled and reproduced. Based on an example video of a natural light effect, a Markov model of the transitions between colors of a single light source representing the effect is learned. The model is a compact, easy to reproduce, and as the user studies show, recognizable representation of the original light effect. The second part of the thesis studies the perceived quality of one of the unique capabilities of LEDs, chromatic temporal transitions. Using psychophysical methods, existing spatial models of human color vision were found to be unsuitable for predicting the visibility of temporal artifacts caused by the digital controls. The chapters in this part demonstrate new perceptual effects and make the first steps towards building a temporal model of human color vision. In chapter 6 the perception of smoothness of digital light transitions is studied. The studies presented demonstrate the dependence of the visibility of digital steps in a temporal transition on the frequency of change, chromaticity, intensity and direction of change of the transition. Furthermore, a clear link between the visibility of digital steps and flicker visibility is demonstrated. Finally, a new, exponential law for the dependence of the threshold speed of smooth transitions on the changing frequency is hypothesized and proven in subsequent experiments. Chapter 7 studies the discrimination and preference of different color transitions between two colors. Due to memory effects, the discrimination threshold for complete transitions was shown to be larger than the discrimination threshold for two single colors. Two linear transitions in different color spaces were shown to be significantly preferred over a set of other, curved, transitions. Chapter 8 studies chromatic and achromatic flicker visibility in the periphery. A complex change of both the absolute visibility thresholds for different frequencies, as well as the critical flicker frequency is observed. Finally, an increase in the absolute visibility thresholds caused by an addition of a mental task in central vision is demonstrated

Physics-based Shading Reconstruction for Intrinsic Image Decomposition

We investigate the use of photometric invariance and deep learning to compute intrinsic images (albedo and shading). We propose albedo and shading gradient descriptors which are derived from physics-based models. Using the descriptors, albedo transitions are masked out and an initial sparse shading map is calculated directly from the corresponding RGB image gradients in a learning-free unsupervised manner. Then, an optimization method is proposed to reconstruct the full dense shading map. Finally, we integrate the generated shading map into a novel deep learning framework to refine it and also to predict corresponding albedo image to achieve intrinsic image decomposition. By doing so, we are the first to directly address the texture and intensity ambiguity problems of the shading estimations. Large scale experiments show that our approach steered by physics-based invariant descriptors achieve superior results on MIT Intrinsics, NIR-RGB Intrinsics, Multi-Illuminant Intrinsic Images, Spectral Intrinsic Images, As Realistic As Possible, and competitive results on Intrinsic Images in the Wild datasets while achieving state-of-the-art shading estimations.Comment: Submitted to Computer Vision and Image Understanding (CVIU