Mutual Occlusion Research Articles

Effective nuclei segmentation with sparse shape prior and dynamic occlusion constraint for glioblastoma pathology images.

We propose a segmentation method for nuclei in glioblastoma histopathologic images based on a sparse shape prior guided variational level set framework. By spectral clustering and sparse coding, a set of shape priors is exploited to accommodate complicated shape variations. We automate the object contour initialization by a seed detection algorithm and deform contours by minimizing an energy functional that incorporates a shape term in a sparse shape prior representation, an adaptive contour occlusion penalty term, and a boundary term encouraging contours to converge to strong edges. As a result, our approach is able to deal with mutual occlusions and detect contours of multiple intersected nuclei simultaneously. Our method is applied to several whole-slide histopathologic image datasets for nuclei segmentation. The proposed method is compared with other state-of-the-art methods and demonstrates good accuracy for nuclei detection and segmentation, suggesting its promise to support biomedical image-based investigations.

The Calibration of Kinect Camera Based on ARtoolkit

To deal with mutual occlusion of the virtual and real objects in the augmented reality scene, their depth values should be compared by using ARtoolkit and Kinect application programming interfaces (APIs). To improve the accuracy of the experiment, it has given up the existing default camera parameters file in ARtoolkit, and calibrated the Kinect camera to generate its own parameters data file. And different from the existing Kinect camera calibration methods, it abandoned the method of using the open NI SDK or extra high definition color camera as an intermediate bridge. Experimental results show this method has achieved a high accuracy and the validity by verifying in ARtoolkit.

Design and prototype of an augmented reality display with per-pixel mutual occlusion capability.

State-of-the-art optical see-through head-mounted displays for augmented reality (AR) applications lack mutual occlusion capability, which refers to the ability to render correct light blocking relationship when merging digital and physical objects, such that the virtual views appear to be ghost-like and lack realistic appearance. In this paper, using off-the-shelf optical components, we present the design and prototype of an AR display which is capable of rendering per-pixel mutual occlusion. Our prototype utilizes a miniature organic light emitting display coupled with a liquid crystal on silicon type spatial light modulator to achieve an occlusion capable AR display offering a 30° diagonal field of view and an angular resolution of 1.24 arcminutes, with an optical performance of > 0.4 contrast over the full field at the Nquist frequency of 24.2 cycles/degree. We experimentally demonstrate a monocular prototype achieving >100:1 dynamic range in well-lighted environments.

Occlusion handling using angular spectrum convolution in fully analytical mesh based computer generated hologram.

Occlusion handling in computer-generated holography is of vast importance as it enhances depth information by presenting correct motion parallax of the 3D scene within the viewing angle. In this paper, we propose a computationally efficient occlusion handling technique based on a fully analytic mesh based computer generated holography. The proposed technique uses angular spectrum convolution that renders exact occlusion while preserving all other aspects of the fully analytic mesh based computer generated holography. The proposed method is computationally efficient as only a single convolution operation is required for each mesh without numerical propagation between the meshes. The proposed method is also exact as it performs the occlusion processing in the tilted mesh plane, being free from artifacts coming from orthographic spatial masking. The proposed method can be applied to the self and the mutual occlusions between the objects in the 3D scene. The computer simulated results show the feasibility of the proposed method.

Deep self-paced learning for person re-identification

Person re-identification (Re-ID) usually suffers from noisy samples with background clutter and mutual occlusion, which makes it extremely difficult to distinguish different individuals across the disjoint camera views. In this paper, we propose a novel deep self-paced learning (DSPL) algorithm to alleviate this problem, in which we apply a self-paced constraint and symmetric regularization to help the relative distance metric training the deep neural network, so as to learn the stable and discriminative features for person Re-ID. Firstly, we propose a soft polynomial regularizer term which can derive the adaptive weights to samples based on both the training loss and model age. As a result, the high-confidence fidelity samples will be emphasized and the low-confidence noisy samples will be suppressed at early stage of the whole training process. Such a learning regime is naturally implemented under a self-paced learning (SPL) framework, in which samples weights are adaptively updated based on both model age and sample loss using an alternative optimization method. Secondly, we introduce a symmetric regularizer term to revise the asymmetric gradient back-propagation derived by the relative distance metric, so as to simultaneously minimize the intra-class distance and maximize the inter-class distance in each triplet unit. Finally, we build a part-based deep neural network, in which the features of different body parts are first discriminately learned in the lower convolutional layers and then fused in the higher fully connected layers. Experiments on several benchmark datasets have demonstrated the superior performance of our method as compared with the state-of-the-art approaches.

Deep ranking model by large adaptive margin learning for person re-identification

Person re-identification aims to match images of the same person across disjoint camera views, which is a challenging problem in video surveillance. The major challenge of this task lies in how to preserve the similarity of the same person against large variations caused by complex backgrounds, mutual occlusions and different illuminations, while discriminating the different individuals. In this paper, we present a novel deep ranking model with feature learning and fusion by learning a large adaptive margin between the intra-class distance and inter-class distance to solve the person re-identification problem. Specifically, we organize the training images into a batch of pairwise samples. Treating these pairwise samples as inputs, we build a novel part-based deep convolutional neural network (CNN) to learn the layered feature representations by preserving a large adaptive margin. As a result, the final learned model can effectively find out the matched target to the anchor image among a number of candidates in the gallery image set by learning discriminative and stable feature representations. Overcoming the weaknesses of conventional fixed-margin loss functions, our adaptive margin loss function is more appropriate for the dynamic feature space. On four benchmark datasets, PRID2011, Market1501, CUHK01 and 3DPeS, we extensively conduct comparative evaluations to demonstrate the advantages of the proposed method over the state-of-the-art approaches in person re-identification.

P‐220L: Late‐News Poster: Demonstration of an Occlusion‐Capable Optical See‐Through Head‐Mounted Display

Most of the state‐of‐the‐art optical see‐though head‐mounted displays lack the ability to occlude the real scene and fail to render the virtual scene in high contrast with bright surroundings. Mutual occlusion is a strong contributor to the human visual system's ability to distinguish depth information and create a seamless integration of the real and virtual world. In this paper, we present a monocular prototype of an occlusion‐capable optical see‐through head mounted display and demonstrate its mutual occlusion capability and performance in bright environments. Our design currently uses an Organic LED (OLED) microdisplay coupled with a Liquid crystal on silicon (LCoS) type Spatial Light Modulator (SLM) to achieve a 30 degree diagonal FOV and 1920x1080 color resolution.

AUTOMATED LEVEL SET SEGMENTATION OF HISTOPATHOLOGIC CELLS WITH SPARSE SHAPE PRIOR SUPPORT AND DYNAMIC OCCLUSION CONSTRAINT.

In this paper, we propose a novel segmentation method for cells in histopathologic images based on a sparse shape prior guided variational level set framework. We automate the cell contour initialization by detecting seeds and deform contours by minimizing a new energy functional that incorporates a shape term involving sparse shape priors, an adaptive contour occlusion penalty term, and a boundary term encouraging contours to converge to strong edges. As a result, our approach is able to accommodate mutual occlusions and detect contours of multiple intersected cells. We apply our algorithm to a set of whole-slide histopathologic images of brain tumor sections. The proposed method is compared with other popular methods, and demonstrates good accuracy for cell segmentation by quantitative measures, suggesting its promise to support biomedical image-based investigations.

Separation and visualization of arteries and heart in 3D computed tomography angiography images

The visualization of arteries and heart usually plays a crucial role in the clinical diagnosis, but researchers face the problems of region selection and mutual occlusion in clinical visualization. Therefore, the arteries and the heart cannot be easily visualized by current visualization methods. To solve the problems, we propose a new framework for arteries and cardiac visualization by combining a priori knowledge and the set operations. Firstly, a suitable region can be easily determined in the transfer function space with a priori knowledge and the visual feedback results. Secondly, the arteries and the heart can be directly extracted by the marked seed point. Finally, the arteries and the heart are separated for solving mutual occlusion through the set operations. This framework can easily solve the mutual occlusion problem in clinical visualization and greatly improve the region selection method in the transfer function space. Its effectiveness has been demonstrated on the basis of many experimental results.

Probabilistic multi-person localisation and tracking in image sequences

The localisation and tracking of persons in image sequences in commonly guided by recursive filters. Especially in a multi-object tracking environment, where mutual occlusions are inherent, the predictive model is prone to drift away from the actual target position when not taking context into account. Further, if the image-based observations are imprecise, the trajectory is prone to be updated towards a wrong position. In this work we address both these problems by using a new predictive model on the basis of Gaussian Process Regression, and by using generic object detection, as well as instance-specific classification, for refined localisation. The predictive model takes into account the motion of every tracked pedestrian in the scene and the prediction is executed with respect to the velocities of neighbouring persons. In contrast to existing methods our approach uses a Dynamic Bayesian Network in which the state vector of a recursive Bayes filter, as well as the location of the tracked object in the image, are modelled as unknowns. This allows the detection to be corrected before it is incorporated into the recursive filter. Our method is evaluated on a publicly available benchmark dataset and outperforms related methods in terms of geometric precision and tracking accuracy.

Multiple Vehicle Tracking using Adaptive Gaussian Mixture Model and Kalman Filter

Based on data from the Central Statistics Agency (BPS) of the Republic of Indonesia in catalog Crime Statistics in 2014, the number of crimes of theft of motor vehicles has increased since 2010 until 2013. Recorded in 2013 the number reached 42.508 cases of theft. In this study, we made a prototype application using Adaptive Gaussian Mixture Models algorithms and the Kalman Filter to detect the movement of the vehicle in order to prevent vehicle theft. Adaptive Gaussian Mixture Models were used for image segmentation foreground and background, while the Kalman Filter was used to track the vehicle. Stages in this study consisted of two phases, namely the manufacture of prototype and testing of prototype applications. Testing was done by observing the resource usage of memory (RAM) and the processor when the application was executed and the speed and degree of vehicle motion detection accuracy. The test results showed that the prototype application using Adaptive Gaussian Mixture Model and Kalman Filter had an accuracy rate of 90% and a high speed in detecting motion with the use of vehicles under 65000 KB RAM and processor work load below 27% on condition vehicles that have mutual occlusion.

Robust tracking of fish schools using CNN for head identification

Tracking individuals in a fish school with video cameras is one of the most effective ways to quantitatively investigate their behavior which is of great value for biological research. However, tracking large numbers of fish with complex non-rigid deformation, similar appearance and frequent mutual occlusions is a challenge task. In this paper we propose an effective tracking method that can reliably track a large number of fish throughout the entire duration. The first step of the proposed method is to detect fish heads using a scale-space method. Data association across frames is achieved via identifying the head image pattern of each individual fish in each frame, which is accomplished by a convolutional neural network (CNN) specially tailored to suit this task. Then the prediction of the motion state and the recognition result by CNN are combined to associate detections across frames. The proposed method was tested on 5 video clips having different number of fish respectively. Experiment results show that the correctness of their identities is not affected by frequent occlusions. The proposed method outperforms two state-of-the-art fish tracking methods in terms of 7 performance metrics.

An effective and robust method for tracking multiple fish in video image based on fish head detection.

BackgroundFish tracking is an important step for video based analysis of fish behavior. Due to severe body deformation and mutual occlusion of multiple swimming fish, accurate and robust fish tracking from video image sequence is a highly challenging problem. The current tracking methods based on motion information are not accurate and robust enough to track the waving body and handle occlusion. In order to better overcome these problems, we propose a multiple fish tracking method based on fish head detection.ResultsThe shape and gray scale characteristics of the fish image are employed to locate the fish head position. For each detected fish head, we utilize the gray distribution of the head region to estimate the fish head direction. Both the position and direction information from fish detection are then combined to build a cost function of fish swimming. Based on the cost function, global optimization method can be applied to associate the target between consecutive frames. Results show that our method can accurately detect the position and direction information of fish head, and has a good tracking performance for dozens of fish.ConclusionThe proposed method can successfully obtain the motion trajectories for dozens of fish so as to provide more precise data to accommodate systematic analysis of fish behavior.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1138-y) contains supplementary material, which is available to authorized users.

A GAUSSIAN PROCESS BASED MULTI-PERSON INTERACTION MODEL

Abstract. Online multi-person tracking in image sequences is commonly guided by recursive filters, whose predictive models define the expected positions of future states. When a predictive model deviates too much from the true motion of a pedestrian, which is often the case in crowded scenes due to unpredicted accelerations, the data association is prone to fail. In this paper we propose a novel predictive model on the basis of Gaussian Process Regression. The model takes into account the motion of every tracked pedestrian in the scene and the prediction is executed with respect to the velocities of all interrelated persons. As shown by the experiments, the model is capable of yielding more plausible predictions even in the presence of mutual occlusions or missing measurements. The approach is evaluated on a publicly available benchmark and outperforms other state-of-the-art trackers.

A GAUSSIAN PROCESS BASED MULTI-PERSON INTERACTION MODEL

Online multi-person tracking in image sequences is commonly guided by recursive filters, whose predictive models define the expected positions of future states. When a predictive model deviates too much from the true motion of a pedestrian, which is often the case in crowded scenes due to unpredicted accelerations, the data association is prone to fail. In this paper we propose a novel predictive model on the basis of Gaussian Process Regression. The model takes into account the motion of every tracked pedestrian in the scene and the prediction is executed with respect to the velocities of all interrelated persons. As shown by the experiments, the model is capable of yielding more plausible predictions even in the presence of mutual occlusions or missing measurements. The approach is evaluated on a publicly available benchmark and outperforms other state-of-the-art trackers.

Multiple Granularity Modeling: A Coarse-to-Fine Framework for Fine-grained Action Analysis

Detecting fine-grained human action from video sequence is challenging. In this work, we propose to decompose this difficult analytic problem into two sequential tasks with increasing granularity. Firstly, we infer the coarse interaction status, i.e., which object is being manipulated and where the interaction occurs. To address the issue of frequent mutual occlusions during manipulation, we propose an interaction tracking framework in which hand (object) position and interaction status are jointly tracked by explicitly modeling the occlusion context. Secondly, for a given query sequence, the inferred interaction status is utilized to efficiently identify a small set of candidate matching sequences from the annotated training set. Frame-level action labels are then transferred to the query sequence by setting up the matching between the query and candidate sequences. Comprehensive experiments on two challenging fine-grained activity datasets show that: (1) the proposed interaction tracking approach achieves high tracking accuracy for multiple mutually occluded objects (hands) during manipulation action; and (2) the proposed multiple granularity analysis framework achieves superior action detection performance improvement over state-of-the-art methods.

Hough Forest-based Association Framework with Occlusion Handling for Multi-Target Tracking

This letter presents a novel multi-target tracking approach consisting of two parts. The first part is the detection based association to form global tracks. Short yet reliable tracklets are firstly generated. By effectively combining appearance and motion information, a Hough forest learning framework is constructed to obtain a more discriminative affinity model and produce longer association between tracklets. In the second part, in order to connect isolate detections for trajectory consistency, we present an appearance similarity model based on mutual occlusion reasoning. A novel fusion feature template is designed to accurately compute the matching score between each isolated detection and target. Experimental results show significant improvements of our method when compared with several state-of-the-art methods.

Segmenting and Tracking Multiple Dividing Targets Using ilastik.

Tracking crowded cells or other targets in biology is often a challenging task due to poor signal-to-noise ratio, mutual occlusion, large displacements, little discernibility, and the ability of cells to divide. We here present an open source implementation of conservation tracking (Schiegg et al., IEEE international conference on computer vision (ICCV). IEEE, New York, pp 2928-2935, 2013) in the ilastik software framework. This robust tracking-by-assignment algorithm explicitly makes allowance for false positive detections, undersegmentation, and cell division. We give an overview over the underlying algorithm and parameters, and explain the use for a light sheet microscopy sequence of a Drosophila embryo. Equipped with this knowledge, users will be able to track targets of interest in their own data.

Tracking Algorithm of Multiple Pedestrians Based on Particle Filters in Video Sequences

Pedestrian tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in pedestrian tracking for nonlinear and non-Gaussian estimation problems. However, pedestrian tracking in complex environment is still facing many problems due to changes of pedestrian postures and scale, moving background, mutual occlusion, and presence of pedestrian. To surmount these difficulties, this paper presents tracking algorithm of multiple pedestrians based on particle filters in video sequences. The algorithm acquires confidence value of the object and the background through extracting a priori knowledge thus to achieve multipedestrian detection; it adopts color and texture features into particle filter to get better observation results and then automatically adjusts weight value of each feature according to current tracking environment. During the process of tracking, the algorithm processes severe occlusion condition to prevent drift and loss phenomena caused by object occlusion and associates detection results with particle state to propose discriminated method for object disappearance and emergence thus to achieve robust tracking of multiple pedestrians. Experimental verification and analysis in video sequences demonstrate that proposed algorithm improves the tracking performance and has better tracking results.

Myosin IIb-dependent Regulation of Actin Dynamics Is Required for N-Methyl-d-aspartate Receptor Trafficking during Synaptic Plasticity

N-Methyl-d-aspartate receptor (NMDAR) synaptic incorporation changes the number of NMDARs at synapses and is thus critical to various NMDAR-dependent brain functions. To date, the molecules involved in NMDAR trafficking and the underlying mechanisms are poorly understood. Here, we report that myosin IIb is an essential molecule in NMDAR synaptic incorporation during PKC- or θ burst stimulation-induced synaptic plasticity. Moreover, we demonstrate that myosin light chain kinase (MLCK)-dependent actin reorganization contributes to NMDAR trafficking. The findings from additional mutual occlusion experiments demonstrate that PKC and MLCK share a common signaling pathway in NMDAR-mediated synaptic regulation. Because myosin IIb is the primary substrate of MLCK and can regulate actin dynamics during synaptic plasticity, we propose that the MLCK- and myosin IIb-dependent regulation of actin dynamics is required for NMDAR trafficking during synaptic plasticity. This study provides important insights into a mechanical framework for understanding NMDAR trafficking associated with synaptic plasticity.