In the tracking-by-detection scheme of multiple object tracking (MOT), the data association process in which existing tracking data and new detections are matched over time is very important. A framework is proposed to solve the data association problem in MOT in scenarios where there are potential target interactions and occlusions in crowded environments. This framework consists of an input layer and an association layer. The input layer is an end-to-end feature-map extraction model that incorporates a simplified Siamese convolutional neural network, which effectively distinguishes similar objects based on their appearance and motion. The association layer is composed of a bidirectional gated recurrent unit network with three layers of fully connected networks (FCNs), whose outputs are fed into the FCNs and transformed into an association matrix that reflects the matching scores between the detections and existing tracks. The matrix is then used to minimize the loss of the framework. The experimental results show that the proposed framework demonstrates outstanding performance for MOT, with its accuracy and precision in MOT reaching values as high as 26.1% and 71.2%, respectively.
Low dose CT is a popular research which focuses to reduce radiation damaging. Inspiring from the aperture coding method in optical imaging, azimuth coding projection method which belongs to the category of incomplete projection is proposed to shorten the exposure time and reduce the projection paths. Based on this coding method, the ROI will inevitably be sampled intensively, the information which is from region of interest (ROI)projection data was modulated by "coding". And the azimuth coding projection methods for the ROI will reflect the spatial continuity of the ROI. The spatial correlation between slice and adjacent slices is strong in human CT image sequences. Deep learning (DL) technology excels in medical image feature extraction. Convolutional neural network(CNN)was used to extract the modulated ROI projection information, and CNN incorporated the spatial information from adjacent slices based on the strong spatial correlation, then the obtained feature map is nonlinearly mapped to the feature map containing less artifacts. After training and testing the CNN, there is one azimuth coding method which are adapted to the corresponding the ROI at least, CT reconstructed images were restored well.
This paper presents a segmented X-ray spectrum detection method based on a layered X-ray detector in Cadmium Telluride (CdTe) substrate. We describe the three-dimensional structure of proposed detector pixel and investigate the matched spectrum-resolving method. Polychromatic X-ray beam enter the CdTe substrate edge on and will be absorbed completely in different thickness varying with photon energy. Discrete potential wells are formed under external controlling voltage to collect the photo-electrons generated in different layers, and segmented X-ray spectrum can be deduced from the quantity of photo-electrons. In this work, we verify the feasibility of the segmented-spectrum detection mechanism by simulating the absorption of monochromatic X-ray in a CdTe substrate. Experiments in simulation show that the number of photo-electrons grow exponentially with the increase of incident thickness, and photons with different energy will be absorbed in various thickness. The charges generated in different layers are collected into adjacent potential wells, and collection efficiency is estimated to be about 87% for different incident intensity under the 40000V/cm electric field. Errors caused by charge sharing between neighboring layers are also analyzed, and it can be considered negligible by setting appropriate size of electrodes.
Image blending plays an important role in video mosaicking, which has a high demand for real-time performance and visual quality. This paper proposes a fast blending method based on Bresenham algorithm, which realizes blending by controlling the storing addresses of source pixels. The starting storing location is accurately computed based on the coordinates of the middle pixel of the seam instead of the first pixel’s, reducing the accumulated error along the seam significantly. The other storing addresses are acquired using a variable-step Bresenham method, which takes advantage of burst mode operation of a dynamic memory and can achieve a good trade-off between the operation convenience and memory requirement. By the proposed method, complicated calculations of storing addresses are simplified into integer additions and subtractions, which is more suitable for hardware implementation. A hardware architecture based on field programmable gate array is presented to evaluate the proposed method with clock frequency analysis and resource assessment. The experimental results show that the proposed method achieves good performance of high image quality, low computational complexity, and low memory requirement.
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