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

The human brain can send a command to external devices or communicate with the outside environment by the means of a brain computer interface (BCI) system. The effectiveness depends on how precisely specific brain activities can be identified from EEG. Noise is usually mixed into the EEG signal, and cannot be separated or filtered out in some cases. In a practical BCI system, the whole segment of EEG is discarded when a portion of that segment is contaminated by extreme noise or artifacts. This leads to the weakness that the BCI system cannot output decoding results during the period of that discarded segment. In order to solve this problem, we employed a Lomb-Scargle periodogram to estimate the spectral power based on an unevenly spaced segment, a portion of which has been removed due to noise contamination. According to the classification results of motor imagery data, the accuracy is not dramatically decreased along with increased proportion of data removal.

Spectral Power Estimation for Unevenly Spaced Motor Imagery Data

This paper investigates the sparse representation of visual neural information and its learning algorithm. First we introduce a generative statistical model for internal representation of visual neural information. Then the neural computing mechanism for representing sensory information in the generative model is discussed, and learning algorithm is developed for training the parameters in the generative model. Finally computer simulations are provided to illustrate the sparseness of the internal representation of the visual information

Sparse Representation and Synaptic Adaptation of the Visual Sensory System

It has been shown that incorporating depth features into RGB features helps
improve semantic segmentation. However, depth information is usually
unavailable for the test images. In this paper, we leverage only the depth of
training images as the privileged information to mine the hard pixels in
semantic segmentation. Specifically, we propose a novel Loss Weight Module
(LWM), which outputs a loss weight map by employing two depth-related
measurements of hard pixels: Depth Prediction Error (DPE) and Depth-aware
Segmentation Error (DSE). The loss weight map is then applied to segmentation
loss, aimed at learning a more robust model by paying more attention to the
hard pixels. Besides, we also explore a curriculum learning strategy based on
the loss weight map. Meanwhile, to fully mine the hard pixels on different
scales, we apply our loss weight module to multi-scale side outputs. Our hard
pixels mining method achieves the state-of-the-art results on two benchmark
datasets, and even outperforms the methods which need depth input while
testing.

Hard Pixels Mining: Learning Using Privileged Information for Semantic Segmentation.

As a common image editing operation, image composition aims to cut the foreground from one image and paste it on another image, resulting in a composite image. However, there are many issues that could make the composite images unrealistic. These issues can be summarized as the inconsistency between foreground and background, which include appearance inconsistency (e.g., incompatible color and illumination) and geometry inconsistency (e.g., unreasonable size and location). Previous works on image composition target at one or more issues. Since each individual issue is a complicated problem, there are some research directions (e.g., image harmonization, object placement) which focus on only one issue. By putting all the efforts together, we can acquire realistic composite images. Sometimes, we expect the composite images to be not only realistic but also aesthetic, in which case aesthetic evaluation needs to be considered. In this survey, we summarize the datasets and methods for the above research directions. We also discuss the limitations and potential directions to facilitate the future research for image composition. Finally, as a double-edged sword, image composition may also have negative effect on our lives (e.g., fake news) and thus it is imperative to develop algorithms to fight against composite images. Datasets and codes for image composition are summarized at this https URL.

Making Images Real Again: A Comprehensive Survey on Deep Image Composition.

. Image cropping aims to find visually appealing crops in an image, which is an important yet challenging task. In this paper, we consider a specific and practical application: human-centric image cropping, which focuses on the depiction of a person. To this end, we propose a human-centric image cropping method with two novel feature designs for the candidate crop: partition-aware feature and content-preserving feature. For partition-aware feature, we divide the whole image into nine partitions based on the human bounding box and treat different partitions in a candidate crop differently conditioned on the human information. For content-preserving feature, we predict a heatmap indicating the important content to be included in a good crop, and extract the geometric relation between the heatmap and a candidate crop. Extensive experiments demonstrate that our method can perform favorably against state-of-the-art image cropping methods on human-centric image cropping task. Code is available at https://github.com/bcmi/ Human-Centric-Image-Cropping .

Liqing Zhang

Papers

Spectral Power Estimation for Unevenly Spaced Motor Imagery Data

Sparse Representation and Synaptic Adaptation of the Visual Sensory System

Hard Pixels Mining: Learning Using Privileged Information for Semantic Segmentation.

Making Images Real Again: A Comprehensive Survey on Deep Image Composition.

Human-centric Image Cropping with Partition-aware and Content-preserving Features