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The target of the depth estimation is to estimate a high quality dense depth map from a single RGB input image. The depth map is an image containing distance information of surface of scene objects from the camera. Depth estimation is crucial for scene understanding, since for accurate scene analysis, more information is helpful. By using the depth estimation, we will have not only the color information from RGB images, but also distance information.

Currently, most depth estimation methods use deep learning by encoder and decoder structure, which are time and computation resources consuming, for example AdaBins[1]. We aim to design a successive subspace learning based method with less computation resources and mathematically explainable, while keeping high performance.

We use the NYU-Depth-v2 dataset[2] for training. We have proposed the method shown in the second image and get some results. In the second image, P represent the RGB images after conversion and D represent the correspondent depth images. In the future, we aim to improve the results by refine the model.

— By Ganning Zhao

Reference:

[1] Bhat, S. F., Alhashim, I., & Wonka, P. (2021). Adabins: Depth estimation using adaptive bins. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4009-4018).

[2] Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012, October). Indoor segmentation and support inference from rgbd images. In European conference on computer vision (pp. 746-760). Springer, Berlin, Heidelberg.

Image credits:

Image showing the architecture of AdaBins is from [1].

Image showing the architecture of our current method.

Nuclei segmentation has been extensively used in biological image analysis for reading histology images from microscopes. The population of nuclei, their shape and density play a significant role in clinical practice for cancer diagnosis and its aggressiveness assessment. The reading and annotation of those images is a fairly laborious and time consuming task, being carried out only from expertised pathologists. As such, the computer aided automation of this process is of high significance, since it reduces the physicians’ work load and provides a more objective segmentation output. Yet, different challenges such as color and intensity variations that result from images of different organs and acquisition settings, hinder the performance improvement of the algorithms.

In past years, both supervised and unsupervised solutions have been proposed to tackle those challenges. Most of the recent approaches [1] adopt deep learning (DL) networks, to directly learn from pathologists’ annotated segmentation masks. However, most of the available datasets have very few samples, relatively to the DL requirements, and also their annotations have reportedly a low inter-annotator rate of agreement. Therefore, it is quite challenging for DL models to generalize well to unseen images from multiple organs. At this point, the motivation behind using an unsupervised method should be obvious. We propose a data driven and parameter free methodology [2], named CBM, for the nuclei segmentation task that requires no training labels. The pipeline begins with a data-driven Color (C) transform, to highlight the nuclei cell regions over the background. Then, a data-driven Binarization (B) process is built on the bi-modal assumption that each local region has two histogram peaks, one corresponding to the background and one to the nuclei areas. We use a locally adaptive threshold to binarize each histology [...]

A knowledge graph is a collection of factual triples (h, r, t) consisting of two entities and one relation. Most knowledge graphs suffer from the incompleteness that there are many missing relations between entities. To predict missing links, each relation is modeled by a binary classifier to predict whether the links between two entities exist or not. Negative sampling is a task to draw negative samples efficiently and effectively from the unobserved triples to train the classifiers. The quality and quantity of the negative samples will highly affect the performance on link prediction.
Naive negative sampling [1] suggests generating negative samples by corrupting one of the entities in the observed triples, e.g. (h’, r, t) or (h, r, t’). Despite the simplicity of naive negative samples, the generated negative samples carry little semantics. For example, given a positive triple (Hulk, movie_genre, Science Fiction), a negative sample (Hulk, movie_genre, New York City) might be generated by naive negative sampling, which will never be a valid triple in the real-world scenario. Instead, we are looking for negative examples, such as (Hulk, movie_genre, Romance), that provide more information to the classifiers. Based on the observation, we only draw the corrupted entities within the set of observed entities that have been linked by the given relation, also known as the ‘range’ for the relation. However, a drawback is that the chance of drawing false negatives is high. Therefore, we further filter the drawn corrupted entities based on the entity-entity co-occurrence. For example, it’s not likely for us to generate a negative sample (Hulk, movie_genre, Adventure) because we know from the dataset that movie genres ‘Science Fiction’ and ‘Adventure’ are highly co-occurred. The first figure shows how the positive [...]

In Fall 2021, we have a new MCL member, Rafael Luiz Testa, joining our big family. Here is a short interview with Rafael with our great welcome.

1. Could you briefly introduce yourself and your research interests?

My name is Rafael Luiz Testa. I received Bachelor’s (2014) and Master’s (2018) degrees in Information Systems from the University of São Paulo, Brazil. I am currently pursuing my PhD degree at the University of São Paulo with a nine-month stay at MCL/USC. I have been working on computer graphics since 2012. In 2013, I joined a project to help people with psychiatric disorders to recognize emotions in facial expressions. My main research interests are image/video analysis and synthesis, as well as facial expression.

2. What is your impression about MCL and USC?

The USC and MCL are both internationally recognized for the quality of their work. I am so happy that I found here at MCL a place where everyone has plenty of opportunities to achieve their best. The MCL Director, Dr. C.-C. Jay Kuo, instigates innovation and collaboration between students. Furthermore, the MCL has such a friendly and kind environment.

3. What is your future expectation and plan in MCL?

I believe my stay at MCL will give me an opportunity to see my research from an entirely novel perspective. I hope I can enjoy to the fullest such an innovative environment. In the future, I would like to be a professor and pursue an academic research career. Thus, I am confident that the work developed at MCL will significantly impact my future projects and help me achieve my career goals.

Misinformation on the Internet and social media, ranging from fake news to fake multimedia such as images and videos, is a significant threat to our society. Effective misinformation detection has become a research focus, driven by commercial and government funding. With the fast-growing deep learning techniques, real-looking fake images can be easily generated using generative adversarial networks (GANs). The problem of fake satellite images detection was recently introduced. Fake satellite images could be generated with the intention of hiding important infrastructure and/or creating fake buildings to deceive others. Although it may be feasible to check whether these images are real or fake using another satellite, the cost is high. Furthermore, the general public and media do not have the proper resource to verify the authenticity of fake satellite images. Consequently, fake satellite images pose serious challenges to our society, as recognized by government organizations concerned about the political and military implications of such technology. Handcrafted features were used for fake satellite image detection, and its best detection performance measured by the F-1 score is 87%. 

A new method, called PSL-DefakeHop, is proposed to detect fake satellite images based on the parallel subspace learning (PSL) framework in this work. The DefakeHop method was developed previously for the detection of Deepfake generated faces under the successive subspace learning (SSL) framework. PSL is proposed to extract features from responses of multiple single-stage filter banks (or called PixelHops), which operate in parallel, and it improves SSL that extracts features from multi-stage cascaded filter banks. PSL has two advantages. First, PSL preserves discriminant features often lie in high-frequency channels, which are however ignored by SSL. Second, decisions from multiple filter banks can be ensembled to further improve detection accuracy. To [...]