A general model for visual motion detection

We propose a general model for detection of both first-order motion and second-order motion. In this model an input stimulus is divided into a number of partially overlapping spatiotemporal local regions. Spatiotemporal frequency analysis is done for every local region using Gabor filters, then the input stimulus (original spatiotemporal signal) is replaced by the outputs of Gabor filters. Local motion is detected by applying Gabor motion detectors to each local spatiotemporal pattern depicted by each local feature value. Outputs of all the detectors are integrated to give the final output for global motion of the input stimulus. The model was simulated on a computer and was confirmed to correctly detect second-order motion as well as first-order motion.     

A novel exploratory method for visual recombination detection

A versatile visual approach for detecting recombination and identifying recombination breakpoints within a sequence alignment is presented. The method is based on two novel diagrams - the highway plot and the occupancy plot - that graphically portray phylogenetic inhomogeneity along an alignment, and can be viewed as a synthesis of two widely used but unrelated methods: bootscanning and quartet-mapping. To illustrate the method, simulated data and HIV-1 and influenza A datasets are investigated.     

Joint level-set and spatio-temporal motion detection for cell segmentation

Background

Cell segmentation is a critical step for quantification and monitoring of cell cycle progression, cell migration, and growth control to investigate cellular immune response, embryonic development, tumorigenesis, and drug effects on live cells in time-lapse microscopy images.

Methods

In this study, we propose a joint spatio-temporal diffusion and region-based level-set optimization approach for moving cell segmentation. Moving regions are initially detected in each set of three consecutive sequence images by numerically solving a system of coupled spatio-temporal partial differential equations. In order to standardize intensities of each frame, we apply a histogram transformation approach to match the pixel intensities of each processed frame with an intensity distribution model learned from all frames of the sequence during the training stage. After the spatio-temporal diffusion stage is completed, we compute the edge map by nonparametric density estimation using Parzen kernels. This process is followed by watershed-based segmentation and moving cell detection. We use this result as an initial level-set function to evolve the cell boundaries, refine the delineation, and optimize the final segmentation result.

Results

We applied this method to several datasets of fluorescence microscopy images with varying levels of difficulty with respect to cell density, resolution, contrast, and signal-to-noise ratio. We compared the results with those produced by Chan and Vese segmentation, a temporally linked level-set technique, and nonlinear diffusion-based segmentation. We validated all segmentation techniques against reference masks provided by the international Cell Tracking Challenge consortium. The proposed approach delineated cells with an average Dice similarity coefficient of 89 % over a variety of simulated and real fluorescent image sequences. It yielded average improvements of 11 % in segmentation accuracy compared to both strictly spatial and temporally linked Chan-Vese techniques, and 4 % compared to the nonlinear spatio-temporal diffusion method.

Conclusions

Despite the wide variation in cell shape, density, mitotic events, and image quality among the datasets, our proposed method produced promising segmentation results. These results indicate the efficiency and robustness of this method especially for mitotic events and low SNR imaging, enabling the application of subsequent quantification tasks.

     

Mechanisms for spatial integration in visual detection: a model based on lateral interactions

Recent studies of visual detection show a configuration dependent weak improvement of thresholds with the number of targets, which corresponds to a fourth-root power law. We find this result to be inconsistent with probability summation models, and account for it by a model of 'physiological' integration that is based on excitatory lateral interactions in the visual cortex. The model explains several phenomena which are confirmed by the experimental data, such as the absence of spatial and temporal uncertainty effects, temporal summation curves, and facilitation by a pedestal in 2AFC tasks. The summation exponents are dependent on the strength of the lateral interactions, and on the distance and orientation relationship between the elements.     

A new electrophoretic-autoradiographic method for the visual detection of phosphotransferases

In this paper we present the details of a slab acrylamide, lanthanum precipitation, autoradiographic technique and show it to be useful for the visualization of at least four different enzymes. We believe that with appropriate separation conditions and reaction mixtures this technique could be extended to a larger number of enzymes, theoretically all those whose isotopically labeled product could be specifically precipitated within the matrix of a polyacrylamide gel. It will be interesting to use this technique with other gel buffer systems, particularly those with a lower pH that have recently been reported (21). In addition, it might be useful to combine it with isoelectric focusing in slab gels (10). The technique would appear to be particularly useful for phosphotransferases and, to date, it has been applied to thymidine kinase and adenosine kinase with encouraging results. Work with other enzymes, and adapting the technique to starch gels, is also in progress.     

A model of visual detection of angular speed for bees

A fly or bee's responses to widefield image motion depend on two basic parameters: temporal frequency and angular speed. Rotational optic flow is monitored using temporal frequency analysers, whereas translational optic flow seems to be monitored in terms of angular speed. Here we present a possible model of an angular speed detector which processes input signals through two parallel channels. The output of the detector is taken as the ratio of the two channels’ outputs. This operation amplifies angular speed sensitivity and depresses temporal frequency tuning. We analyse the behaviour of two versions of this model with different filtering properties in response to a variety of input signals. We then embody the detector in a simulated agent's visual system and explore its behaviour in experiments on speed control and odometry. The latter leads us to suggest a new algorithm for optic flow driven odometry.     

Three-dimensional mathematical reconstruction of the spinal shape, based on active contours

To reduce the amount of radiographs needed for patients with a scoliosis, a radiation-free method based on topographic images of the back was developed. An active contour model simulating spinal stiffness has been applied to video rasterstereographic (VRS) data. The aim of the present study is (a) to evaluate the applicability of active contours to improve the accuracy and the reliability of the three-dimensional (3D) spinal midline reconstruction from back surface data and (b) to design a more robust method to detect the spinal midline. To evaluate the reliability and accuracy, the active contour-based method is compared to a conventional procedure, which has been specifically developed for scoliosis; both methods produce a 3D curve of the spinal midline. The frontal projections and surface rotations of these spinal midlines are compared; r.m.s. deviations of 0.9 mm between the frontal curves and 0.4 degrees between the surface rotations were obtained. Applying the active contour-based method does therefore not result in a substantial difference in accuracy to the conventional procedure. As a conclusion the active contour method is a valuable mathematical method that can accurately reconstruct the spinal midline based on back surface data. In addition, the method can be applied to various postures.     

A model for signal detection based on an adaptive filter

A model for the detection of brief stimuli based on a change detection algorithm and a random walk traversed by the residuals generated by an adaptive filter is proposed. A linear relationship between mean RT and response proportion measures obtained in a simulation of the model was consistent with data obtained in psychophysical discrimination tasks using human observers. In this way the role of the sensory system as a detector of change in ambient stimulation could be incorporated into a signal detection model.     

A CFD level-set method for soft tissue growth: theory and fundamental equations

A level-set method, specifically conceived for the case of soft organic tissue growth from feeding solutions, is introduced and described in detail. The model can handle the morphological evolution of the organic specimen under the influence of external convection (fluid-dynamics of the bioreactor). The analogies and differences between this technique and a previous volume of fraction method are discussed pointing out advantages and limitations of both formulations.     

基于目标检测的东北虎个体自动识别

东北虎个体的自动识别是种群数量评估和制定有效保护策略的重要基础。以东北虎林园和怪坡虎园38 只虎为研究对象,将目标检测方法首次应用到东北虎个体识别研究中,采用多种深度卷积神经网络模型,以实现虎个体的自动识别。首先通过相机在不同角度对 38 只东北虎进行拍摄取样,建立包含13579张图像的虎样本数据集。由于虎的体侧条纹信息不具有对称性,所以运用单次多盒目标检测（Single Shot MultiBox Detector, SSD）方法,对虎的躯干左侧条纹、右侧条纹以及脸部等不同部位图像,进行自动检测并分割提取,极大节省手工截取时间。在检测分割出的左右侧及脸部不同部位图片基础上,运用上、下、左、右平移变换进行数据增强,使图片数目扩大为原来的5 倍。采用LeNet、AlexNet、ZFNet、VGG16、ResNet34共5 种卷积神经网络模型进行个体自动识别。为了提高识别准确率,运用平均值和最大值不同组合方式来优化池化操作,并在全连接层引入概率分别为0.1、0.2、0.3、0.4的丢弃（Dropout）操作防止过拟合。实验表明,目标检测模型耗时较少,截取分割老虎不同部位条纹能达到0.6 s/张,远快于人工截取速度,并且在测试集上准确率能达到97.4%。不同姿态下的目标部位都能正确识别并分割。ResNet34模型的准确率优于其他网络模型,左右侧条纹以及脸部图像识别准确率分别为93.75%、97.01%和 86.28%,右侧条纹识别准确率优于左侧条纹和脸部图像。研究为野生虎自动相机影像的识别提供技术参考。在未来研究中,对东北虎个体影响数据进行扩充,选取更多影像数据进行训练,使网络具有更强的适应性,从而实现更准确的个体识别。     

Statistical region-based active contours for segmentation: An overview

In this paper we propose a brief survey on geometric variational approaches and more precisely on statistical region-based active contours for medical image segmentation. In these approaches, image features are considered as random variables whose distribution may be either parametric, and belongs to the exponential family, or non-parametric estimated with a kernel density method. Statistical region-based terms are listed and reviewed showing that these terms can depict a wide spectrum of segmentation problems. A shape prior can also be incorporated to the previous statistical terms. A discussion of some optimization schemes available to solve the variational problem is also provided. Examples on real medical images are given to illustrate some of the given criteria.     

On the contours of apparent motion: A new perspective on visual space-time

Previous results on the perception of motion indicate that perceived motion paths cannot be explained solely in terms of simple feature-specific analyzers. This is particularly true of apparent (phi) motion. In this paper we develop a dynamic network, with simple filtering and summation properties, which can predict the geometric paths of apparent motion in various spatio-temporal configurations. The network assumptions predict a non-Euclidean metric for the visual space-time of motion perception and we consider the implications of such distortions for various visual displays, including illusions.     

Feature blindness: A challenge for understanding and modelling visual object recognition

Humans rely heavily on the shape of objects to recognise them. Recently, it has been argued that Convolutional Neural Networks (CNNs) can also show a shape-bias, provided their learning environment contains this bias. This has led to the proposal that CNNs provide good mechanistic models of shape-bias and, more generally, human visual processing. However, it is also possible that humans and CNNs show a shape-bias for very different reasons, namely, shape-bias in humans may be a consequence of architectural and cognitive constraints whereas CNNs show a shape-bias as a consequence of learning the statistics of the environment. We investigated this question by exploring shape-bias in humans and CNNs when they learn in a novel environment. We observed that, in this new environment, humans (i) focused on shape and overlooked many non-shape features, even when non-shape features were more diagnostic, (ii) learned based on only one out of multiple predictive features, and (iii) failed to learn when global features, such as shape, were absent. This behaviour contrasted with the predictions of a statistical inference model with no priors, showing the strong role that shape-bias plays in human feature selection. It also contrasted with CNNs that (i) preferred to categorise objects based on non-shape features, and (ii) increased reliance on these non-shape features as they became more predictive. This was the case even when the CNN was pre-trained to have a shape-bias and the convolutional backbone was frozen. These results suggest that shape-bias has a different source in humans and CNNs: while learning in CNNs is driven by the statistical properties of the environment, humans are highly constrained by their previous biases, which suggests that cognitive constraints play a key role in how humans learn to recognise novel objects.     

A simple method for detection of imprinting effects based on case-parents trios

Using data from families in which marker genotypes are known for the father, the mother and the affected offspring, a simple statistic for testing for imprinting effects is developed. The statistic considers whether the expected number of families in which the father carries more copies of a particular marker allele than the mother is equal to the expected number of families in which the mother carries more copies of the allele than the father. The proposed parent-of-origin effects test statistic (POET) is shown to be normally distributed and can be employed to test for imprinting in situations where the marker locus need not be a disease susceptibility locus and where the female and male recombination fractions are sex-specific. A simulation study is conducted to characterize the power of the POET and other properties, and its results show that it is appropriate to employ the POET.     

A biplanar reconstruction method based on 2D and 3D contours: application to the distal femur

A three-dimensional (3D) reconstruction algorithm based on contours identification from biplanar radiographs is presented. It requires, as technical prerequisites, a method to calibrate the biplanar radiographic environment and a surface generic object (anatomic atlas model) representing the structure to be reconstructed. The reconstruction steps consist of: the definition of anatomical regions, the identification of 2D contours associated to these regions, the calculation of 3D contours and projection onto the radiographs, the associations between points of the X-rays contours and points of the projected 3D contours, the optimization of the initial solution and the optimized object deformation to minimize the distance between X-rays contours and projected 3D contours. The evaluation was performed on 8 distal femurs comparing the 3D models obtained to CT-scan reconstructions. Mean error for each distal femur was 1 mm.     

A neural network model for visual motion detection that can explain psychophysical and neurophysiological phenomena

This paper proposes a new neural network model for visual motion detection. The model can well explain both psychophysical findings (the changes of displacement thresholds with stimulus velocity and the perception of apparent motion) and neurophysiological findings (the selectivity for the direction and the velocity of a moving stimulus). To confirm the behavior of the model, numerical examinations were conducted. The results were consistent with both psychophysical and neurophysiological findings.