An iterative image-based inter-frame motion compensation method for dynamic brain PET imaging

As a non-invasive imaging tool, Positron Emission Tomography (PET) plays an important role in brain science and disease research. Dynamic acquisition is one way of brain PET imaging. Its wide application in clinical research has often been hindered by practical challenges, such as patient involuntary movement, which could degrade both image quality and the accuracy of the quantification. This is even more obvious in scans of patients with neurodegeneration or mental disorders. Conventional motion compensation methods were either based on images or raw measured data, were shown to be able to reduce the effect of motion on the image quality. As for a dynamic PET scan, motion compensation can be challenging as tracer kinetics and relatively high noise can be present in dynamic frames. In this work, we propose an image-based inter-frame motion compensation approach specifically designed for dynamic brain PET imaging. Our method has an iterative implementation that only requires reconstructed images, based on which the inter-frame subject movement can be estimated and compensated. The method utilized tracer-specific kinetic modelling and can deal with simple and complex movement patterns. The synthesized phantom study showed that the proposed method can compensate for the simulated motion in scans with 18F-FDG, 18F-Fallypride and 18F-AV45. Fifteen dynamic 18F-FDG patient scans with motion artifacts were also processed. The quality of the recovered image was superior to the one of the non-corrected images and the corrected images with other image-based methods. The proposed method enables retrospective image quality control for dynamic brain PET imaging, hence facilitates the applications of dynamic PET in clinics and research.

Effect of service differentiation on QoS in IEEE 802.11e enhanced distributed channel access: a simulation approach

The enhanced distributed channel access (EDCA) protocol is a supplement to IEEE 802.11 medium access control (MAC), ratified by IEEE 802.11e task group to support quality of service (QoS) requirements of both data and real-time applications. Previous research show that it supports priority scheme for multimedia traffic but strict QoS is not guaranteed. This can be attributed to inappropriate tuning of the medium access parameters. Thus, an in-depth analysis of the EDCA protocol and ways of tuning medium access parameters to improve QoS requirements for multimedia traffic is presented in this work. An EDCA model was developed and simulated using MATLAB to assess the effect of differentiating contention window (CW) and arbitration inter-frame space (AIFS) of different traffic on QoS parameters. The optimal performance, delay, and maximum sustainable throughput for each traffic type were computed under saturation load. Insight shows that traffic with higher priority values acquired most of the available channels and starved traffic with lower priority values. The AIFS has more influence on the QoS of EDCA protocol. It was also observed that small CW values generate higher packet drops and collision rate probability. Thus, EDCA protocol provides mechanism for service differentiation which strongly depends on channel access parameters: CW sizes and AIFS.

Compressive Video Sensing Based on Intra-Inter-Frame Constraints and Genetic Algorithm

Establishing structured reconstruction models and efficient reconstruction algorithms according to practical engineering needs is of great concern in the applied research of Compressed Sensing (CS) theory. Targeting problems during high-speed video capture, the paper proposes a set of video CS scheme based on intra-frame and inter-frame constraints and Genetic Algorithm (GA). Firstly, it employs the intra-frame and inter-frame correlation of the video signals as the priori information, creating a video CS reconstruction model on the basis of temporal and spatial similarity constraints. Then it utilizes overcomplete dictionary of Ridgelet to divide the video frames into three structures, smooth, single-oriented, or multijointed. Video frames cluster according to the structure using Affinity Propagation (AP) algorithm, and finally clusters are reconstructed using evolutionary algorithm. It is proved efficient in terms of reconstruction result in the experiment.

The Acquisition of Position and Orientation of the Conveyor Belt Workpiece Based on the Inter Frame Difference in ROI in Camera Video

Conveyor belt transfer is a widely used transportation means in industry and agriculture, with the help of the robot arms the workpiece on the belt can be picked and placed, replacing human sorters for production lines work. The position and orientation of the workpiece are important for grabbing by the robot arms. The goal of the paper was to investigate the acquisition of the position and orientation of the conveyor belt workpiece by means of the camera video overhead looking down the belt. The proposed method is the inter frame difference in nature, using the conveyor belt background as the first frame, but the other frames were not used wholly as usually, only an ROI all around the conveyor belt in the camera video was chosen, and the inter frame difference was carried out in the ROI. The ROI was of the same width as that of the belt in the video which was known in advance, while the length of the ROI was arbitrary, so one pixel in the frame was scaled to the actual length conveniently. Every read frame behind the background was computed the difference with the background in such ROI, and the four vertexes coordinates of the rectangle workpiece image on the belt were obtained when it passed the ROI, and then the distance apart from the right belt boundary was calculated due to the proportional relation between the width of workpiece and that of the ROI. Two kind workpiece orientation on the belt toward the left and right were judged using the same obtained four vertexes coordinates by means of Euclidian length, and the tilt angle was calculated by arc tangent function in favour of two narrow sides of rectangle workpiece grab. The actual test showed that the method of obtaining the position and orientation of workpiece on the belt proposed in the paper could be realized correctly.

Rectangling irregular videos by optimal spatio-temporal warping

Image and video processing based on geometric principles typically changes the rectangular shape of video frames to an irregular shape. This paper presents a warping based approach for rectangling such irregular frame boundaries in space and time, i.e., making them rectangular again. To reduce geometric distortion in the rectangling process, we employ content-preserving deformation of a mesh grid with line structures as constraints to warp the frames. To conform to the original inter-frame motion, we keep feature trajectory distribution as constraints during motion compensation to ensure stability after warping the frames. Such spatially and temporally optimized warps enable the output of regular rectangular boundaries for the video frames with low geometric distortion and jitter. Our experiments demonstrate that our approach can generate plausible video rectangling results in a variety of applications.

A MODEL AND TRAINING METHOD FOR CONTEXT CLASSIFICATION IN CCTV SEWER INSPECTION VIDEO FRAMES

Context. A model and training method for observational context classification in CCTV sewer inspection vide frames was developed and researched. The object of research is the process of detection of temporal-spatial context during CCTV sewer inspections. The subjects of the research are machine learning model and training method for classification analysis of CCTV video sequences under the limited and imbalanced training dataset constraint. Objective. Stated research goal is to develop an efficient context classifier model and training algorithm for CCTV sewer inspection video frames under the constraint of the limited and imbalanced labeled training set. Methods. The four-stage training algorithm of the classifier is proposed. The first stage involves training with soft triplet loss and regularisation component which penalises the network’s binary output code rounding error. The next stage is needed to determine the binary code for each class according to the principles of error-correcting output codes with accounting for intra- and interclass relationship. The resulting reference vector for each class is then used as a sample label for the future training with Joint Binary Cross Entropy Loss. The last machine learning stage is related to decision rule parameter optimization according to the information criteria to determine the boundaries of deviation of binary representation of observations for each class from the corresponding reference vector. A 2D convolutional frame feature extractor combined with the temporal network for inter-frame dependency analysis is considered. Variants with 1D Dilated Regular Convolutional Network, 1D Dilated Causal Convolutional Network, LSTM Network, GRU Network are considered. Model efficiency comparison is made on the basis of micro averaged F1 score calculated on the test dataset. Results. Results obtained on the dataset provided by Ace Pipe Cleaning, Inc confirm the suitability of the model and method for practical use, the resulting accuracy equals 92%. Comparison of the training outcome with the proposed method against the conventional methods indicated a 4% advantage in micro averaged F1 score. Further analysis of the confusion matrix had shown that the most significant increase in accuracy in comparison with the conventional methods is achieved for complex classes which combine both camera orientation and the sewer pipe construction features. Conclusions. The scientific novelty of the work lies in the new models and methods of classification analysis of the temporalspatial context when automating CCTV sewer inspections under imbalanced and limited training dataset conditions. Training results obtained with the proposed method were compared with the results obtained with the conventional method. The proposed method showed 4% advantage in micro averaged F1 score. It had been empirically proven that the use of the regular convolutional temporal network architecture is the most efficient in utilizing inter-frame dependencies. Resulting accuracy is suitable for practical use, as the additional error correction can be made by using the odometer data.

Human Action Recognition Using Spatio-Temporal Multiplier Network and Attentive Correlated Temporal Feature

Nowadays, action recognition has gained more attention from the computer vision community. Normally for recognizing human actions, spatial and temporal features are extracted. Two-stream convolutional neural network is used commonly for human action recognition in videos. In this paper, Adaptive motion Attentive Correlated Temporal Feature (ACTF) is used for temporal feature extractor. The temporal average pooling in inter-frame is used for extracting the inter-frame regional correlation feature and mean feature. This proposed method has better accuracy of 96.9% for UCF101 and 74.6% for HMDB51 datasets, respectively, which are higher than the other state-of-the-art methods.