Cayley Rotation Averaging: Multiple Camera Averaging Under the Cayley Framework.

Rotation averaging, which aims to calculate the absolute rotations of a set of cameras from a redundant set of their relative rotations, is an important and challenging topic arising in the study of structure from motion. A central problem in rotation averaging is how to alleviate the influence of noise and outliers. Addressing this problem, we investigate rotation averaging under the Cayley framework in this paper, inspired by the extra-constraint-free nature of the Cayley rotation representation.

Chromosome classification via deep learning and its application to patients with structural abnormalities of chromosomes.

Background And Objective: Karyotyping is an important technique in cytogenetic practice for the early diagnosis of genetic diseases. Clinical karyotyping is tedious, time-consuming, and error-prone. The objective of our study was to develop a single-stage deep convolutional neural networks (DCNN)-based model to automatically classify normal and abnormal chromosomes in an end-to-end manner.

An Iterative Co-Training Transductive Framework for Zero Shot Learning.

In zero-shot learning (ZSL) community, it is generally recognized that transductive learning performs better than inductive one as the unseen-class samples are also used in its training stage. How to generate pseudo labels for unseen-class samples and how to use such usually noisy pseudo labels are two critical issues in transductive learning. In this work, we introduce an iterative co-training framework which contains two different base ZSL models and an exchanging module.

Non-uniqueness Phenomenon of Object Representation in Modeling IT Cortex by Deep Convolutional Neural Network (DCNN).

Recently DCNN (Deep Convolutional Neural Network) has been advocated as a general and promising modeling approach for neural object representation in primate inferotemporal cortex. In this work, we show that some inherent non-uniqueness problem exists in the DCNN-based modeling of image object representations. This non-uniqueness phenomenon reveals to some extent the theoretical limitation of this general modeling approach, and invites due attention to be taken in practice.

Robust Camera Translation Estimation via Rank Enforcement.

Camera translation averaging, aiming to recover the global camera locations from a given set of camera translation directions, is a challenging problem for Structure from Motion (SfM) in the field of computer vision, largely due to the fact that the given relative translation directions from a set of noisy essential matrices are generally of low accuracy. To tackle this problem, we first reveal a novel but a simple property of the camera translation matrix consisting of all the pairwise camera translations among an arbitrary set of cameras that the rank of this translation matrix is always smaller or equal to 4. Then, by explicitly enforcing this rank property, a novel translation estimation method for computing global camera locations is proposed, called TERE.

Latent-Smoothness Nonrigid Structure From Motion by Revisiting Multilinear Factorization.

How to implement an effective factorization for nonrigid structure from motion (NRSFM) has attracted much attention in recent years. A straightforward factorization scheme is to multilinearly solve NRSFM in an alternating manner, where each of the unknown variables in NRSFM is updated by fixing the others at each iteration. However, recent works show that most existing multilinear factorization (MLF) methods achieve poorer performances than some state-of-the-art sequential factorization methods.

Statistics of Visual Responses to Image Object Stimuli from Primate AIT Neurons to DNN Neurons.

Under the goal-driven paradigm, Yamins et al. ( 2014 ; Yamins & DiCarlo, 2016 ) have shown that by optimizing only the final eight-way categorization performance of a four-layer hierarchical network, not only can its top output layer quantitatively predict IT neuron responses but its penultimate layer can also automatically predict V4 neuron responses. Currently, deep neural networks (DNNs) in the field of computer vision have reached image object categorization performance comparable to that of human beings on ImageNet, a data set that contains 1.

Comparison of IT Neural Response Statistics with Simulations.

Lehky et al. (2011) provided a statistical analysis on the responses of the recorded 674 neurons to 806 image stimuli in anterior inferotemporalm (AIT) cortex of two monkeys. In terms of kurtosis and Pareto tail index, they observed that the population sparseness of both unnormalized and normalized responses is always larger than their single-neuron selectivity, hence concluded that the critical features for individual neurons in primate AIT cortex are not very complex, but there is an indefinitely large number of them.

Effects of exogenous methyl jasmonate on the biosynthesis of shikonin derivatives in callus tissues of Arnebia euchroma.

The shikonin derivatives, accumulated in the roots of Arnebia euchroma (Boraginaceae), showed antibacterial, anti-inflammatory, and anti-tumor activities. To explore their possible biosynthesis regulation mechanism, this paper investigated the effects of exogenous methyl jasmonate (MJ) on the biosynthesis of shikonin derivatives in callus cultures of A. euchroma.

Weighted similarity-invariant linear algorithm for camera calibration with rotating 1D objects.

In this paper, a weighted similarity-invariant linear algorithm for camera calibration with rotating 1D objects is proposed. First, we propose a new estimation method for computing the relative depth of the free endpoint on the 1D object and prove its robustness against noise compared with those used in previous literature. The introduced estimator is invariant to image similarity transforms, resulting in a similarity-invariant linear calibration algorithm which is slightly more accurate than the well-known normalized linear algorithm.