Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. In this paper, we introduce a method for salient region detection that outputs full resolution saliency maps with well-defined boundaries of salient objects. These boundaries are preserved by retaining substantially more frequency content from the original image than other existing techniques. Our method exploits features of color and luminance, is simple to implement, and is computationally efficient. We compare our algorithm to five state-of-the-art salient region detection methods with a frequency domain analysis, ground truth, and a salient object segmentation application. Our method outperforms the five algorithms both on the ground-truth evaluation and on the segmentation task by achieving both higher precision and better recall.

/pdf/frequency-tuned-salient-region-detection-4po18ylvds.pdf

Frequency-tuned salient region detection

Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

https://mmcheng.net/mftp/SalObj/PosterSaliency.pdf

Global contrast based salient region detection

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

In this paper, we discuss multisensory information representation and associative learning in multimodal integration. First, we provide a brief overview of anatomical structure and neural information pathways of multimodal cortexes. Then we formulate the multimodal integration problem into a framework of statistical learning. The associative learning algorithm is proposed to adapt the representation matrix and the sparseness of representation is also adapted in the statistical learning model

Sparse Representation and Associative Learning in Multisensory Integration

A pervasive problem in neuroscience is to determine which regions of the brain are active, given electroencephalographic (EEG) recordings at the scalp. Analysis of evoked potentials in EEG recordings is important approach to reveal the relation between brain functions and structures. In this paper, we present a new approach for evoked potentials extraction and localization, suggesting to explore the high order statistics and temporal structures of the evoked potentials. Efficient learning algorithm is developed for training the demixing model and temporal filters. Theoretic analysis and computer simulations are given to show performance and efficiency of the proposed temporal independent component analysis.

Temporal independent component analysis for separating event-related potentials

In this paper, we integrate orthogonal frequency-division multiplexing (OFDM) technique with vertical Bell Labs layered space-time (V-BLAST) architecture as a promising solution for enhancing the data rates of wireless communication systems, and propose a new blind deconvolution method. A two-stage algorithm is developed to estimate the channel parameters. At first stage, we propose an algorithm based on the second order statistics to decorrelate the sensor signals. After decorrelation, we apply instantaneous demixing algorithm to separate the signals at the second stage. Simulation results demonstrate the validity and the performance of the proposed algorithms.

Two-Stage blind deconvolution for V-BLAST OFDM system

Multi-Task BCI for Online Game Control

Image composition is an important operation in image processing, but the inconsistency between foreground and background significantly degrades the quality of composite image. Image harmonization, which aims to make the foreground compatible with the background, is a promising yet challenging task. However, the lack of high-quality public dataset for image harmonization, which significantly hinders the development of image harmonization techniques. Therefore, we create synthesized composite images based on existing COCO (resp., Adobe5k, day2night) dataset, leading to our HCOCO (resp., HAdobe5k, Hday2night) sub-dataset. To enrich the diversity of our datasets, we also generate synthesized composite images based on our collected Flick images, leading to our HFlickr sub-dataset. All four sub-datasets are released in this https URL.

Liqing Zhang

Papers

Sparse Representation and Associative Learning in Multisensory Integration

Temporal independent component analysis for separating event-related potentials

Two-Stage blind deconvolution for V-BLAST OFDM system

Multi-Task BCI for Online Game Control

Image Harmonization Datasets: HCOCO, HAdobe5k, HFlickr, and Hday2night.