Robust Non-Rigid Registration with Reweighted Position and Transformation Sparsity

Non-rigid registration is challenging because it is ill-posed with high degrees of freedom and is thus sensitive to noise and outliers. We propose a robust non-rigid registration method using reweighted sparsities on position and transformation to estimate the deformations between 3-D shapes. We formulate the energy function with position and transformation sparsity on both the data term and the smoothness term, and define the smoothness constraint using local rigidity. The double sparsity based non-rigid registration model is enhanced with a reweighting scheme, and solved by transferring the model into four alternately-optimized subproblems which have exact solutions and guaranteed convergence. Experimental results on both public datasets and real scanned datasets show that our method outperforms the state-of-the-art methods and is more robust to noise and outliers than conventional non-rigid registration methods.Comment: IEEE Transactions on Visualization and Computer Graphic

NUCKS1 promotes breast cancer cell proliferation and metastasis via PI3K/ AKT pathway

Purpose: To investigate the role of nuclear casein kinase and cyclin-dependent kinase substrate 1 (NUCKS1) in breast cancer. Methods: Breast cancer cells were maintained in RPMI-1640 medium containing 10 % fetal bovine serum in a incubator at 37 °C. Cell proliferation was determined by CCK8 and colony formation assays. Flow cytometry and Transwell assays were used to determine cell cycle and metastasis, respectively. Results: Expression of NUCKS1 was significantly elevated in breast cancer (p < 0.01). Overexpression of NUCKS1 significantly increased cell viability (p < 0.01), and promoted proliferation of breast cancer cells. Knockdown of NUCKS1 inhibited cell proliferation, and induced cell cycle arrest at G1 phase. However, overexpression of NUCKS1 promoted cell cycle progression via down-regulation of p21 and up-regulation of cyclin D1 and CDK1. Cell migration and invasion were induced by overexpression of NUCKS1, and suppressed by silencing of NUCKS1. Overexpression of NUCKS1 enhanced p-AKT and p-PI3K expression, while knockdown of NUCKS1 reduced the expression of p-AKT and p-PI3K in breast cancer cells. Conclusion: NUCKS1 promotes breast cancer cell proliferation and metastasis via activation of PI3K/AKT signaling. The silencing of NUCKS1 can be used as a strategy to develop therapies for the management of breast cancer

Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish: a review

Fish biomass estimation is one of the most common and important practices in aquaculture. The regular acquisition of fish biomass information has been identified as an urgent need for managers to optimize daily feeding, control stocking densities and ultimately determine the optimal time for harvesting. However, it is difficult to estimate fish biomass without human intervention because fishes are sensitive and move freely in an environment where visibility, lighting and stability are uncontrollable. Until now, fish biomass estimation has been mostly based on manual sampling, which is usually invasive, time‐consuming and laborious. Therefore, it is imperative and highly desirable to develop a noninvasive, rapid and cost‐effective means. Machine vision, acoustics, environmental DNA and resistivity counter provide the possibility of developing nonintrusive, faster and cheaper methods for in situ estimation of fish biomass. This article summarizes the development of these nonintrusive methods for fish biomass estimation over the past three decades and presents their basic concepts and principles. The strengths and weaknesses of each method are analysed and future research directions are also presented. Studies show that the applications of information technology such as advanced sensors and communication technologies have great significance to accelerate the development of new means and techniques for more effective biomass estimation. However, the accuracy and intelligence still need to be improved to meet intensive aquaculture requirements. Through close cooperation between fisheries experts and engineers, the precision and the level of intelligence for fish biomass estimation will be further improved based on the above methods

A new method for fish-disease diagnostic problem solving based on parsimonious covering theory and fuzzy inference model

There are three kinds of uncertainty in the process of fish-disease diagnosis, such as randomicity, fuzzy and imperfection, which affect the veracity of fish-disease diagnostic conclusion. So, it is important to construct a fish-disease diagnostic model to effectively deal with these uncertainty knowledge’s representation and reasoning. In this paper, the well-developed parsimonious covering theory capable of handling randomicity knowledge is extended. A fuzzy inference model capable of handling fuzzy knowledge is proposed, and the corresponding algorithms based the sequence of obtaining manifestations are provided to express imperfection knowledge. In the last, the model is proved to be effective and practicality through a set of fish-disease diagnostic casesIFIP International Conference on Artificial Intelligence in Theory and Practice - Expert SystemsRed de Universidades con Carreras en Informática (RedUNCI

Global alignment of deformable objects captured by a single RGB-D camera

We present a novel global registration method for deformable objects captured using a single RGB-D camera. Our algorithm allows objects to undergo large non-rigid deformations, and achieves high quality results without constraining the actor's pose or camera motion. We compute the deformations of all the scans simultaneously by optimizing a global alignment problem to avoid the well-known loop closure problem, and use an as-rigid-as-possible constraint to eliminate the shrinkage problem of the deformed model. To attack large scale problems, we design a coarse-to-fine multi-resolution scheme, which also avoids the optimization being trapped into local minima. The proposed method is evaluated on public datasets and real datasets captured by an RGB-D sensor. Experimental results demonstrate that the proposed method obtains better results than the state-of-the-art methods

Toward developing a tele-diagnosis system on fish disease

Fish disease diagnosis is a complicated process and requires high level of expertise, an expert system for fish disease diagnosis is considered as an effective tool to help fish farmers. However, many farmers have no computers and are not able to access the Internet. Telephone and mobile uses increase rapidly, so, the provision of call centre service appears as a sound alternative support channel for farmer to acquire counseling and support. This paper presents a research attempt to develop and evaluate a call center oriented Hybrid disease diagnosis & consulting system (H-Vet) in aquaculture in China. This paper looks at why H-Vet is needed and what are the advantages and difficulties in the developing and using such a system. A machine learning approach is adopted, which helps to acquire knowledge when enhancing expert systems with the user information collected through call center. This paper also proposes a fuzzy Group Support Systems (GSS) framework for acquiring knowledge from individual expert and aggregating knowledge into workgroup knowledge by H-Vet in the situation of difficult disease diagnosis. The system’s architecture and components are describedIFIP International Conference on Artificial Intelligence in Theory and Practice - Expert SystemsRed de Universidades con Carreras en Informática (RedUNCI

Development of In Situ Sensors for Chlorophyll Concentration Measurement

Chlorophyll fluorescence measurement is a sensitive and effective method to quantify and analyze freshwater and sea water phytoplankton in situ. Major improvements in optical design, electronic technology, and calibration protocol have increased the accuracy and reliability of the fluorometer. This review briefly describes the improvement of probe design, excitation light sources, detectors, and calibrations of in situ fluorometers. Firstly, various optical designs for increasing the efficiency of fluorescence measurement are discussed. Next, the development of electronic technology to meet and improve in situ measurement, including various light sources, detectors, and corresponding measurement protocols, is described. In addition, various calibration materials, procedures, and methods are recommended for different kinds of water. The conclusion discusses key trends and future perspectives for in situ fluorescence sensors

Global 3D non-rigid registration of deformable objects using a single RGB-D camera

We present a novel global non-rigid registration method for dynamic 3D objects. Our method allows objects to undergo large non-rigid deformations, and achieves high quality results even with substantial pose change or camera motion between views. In addition, our method does not require a template prior and uses less raw data than tracking based methods since only a sparse set of scans is needed. We compute the deformations of all the scans simultaneously by optimizing a global alignment problem to avoid the well-known loop closure problem, and use an as-rigid-as-possible constraint to eliminate the shrinkage problem of the deformed shapes, especially near open boundaries of scans. To cope with large-scale problems, we design a coarse-to-fine multi-resolution scheme, which also avoids the optimization being trapped into local minima. The proposed method is evaluated on public datasets and real datasets captured by an RGB-D sensor. Experimental results demonstrate that the proposed method obtains better results than several state-of-the-art methods