27 research outputs found
Occupancy Planes for Single-view RGB-D Human Reconstruction
Single-view RGB-D human reconstruction with implicit functions is often
formulated as per-point classification. Specifically, a set of 3D locations
within the view-frustum of the camera are first projected independently onto
the image and a corresponding feature is subsequently extracted for each 3D
location. The feature of each 3D location is then used to classify
independently whether the corresponding 3D point is inside or outside the
observed object. This procedure leads to sub-optimal results because
correlations between predictions for neighboring locations are only taken into
account implicitly via the extracted features. For more accurate results we
propose the occupancy planes (OPlanes) representation, which enables to
formulate single-view RGB-D human reconstruction as occupancy prediction on
planes which slice through the camera's view frustum. Such a representation
provides more flexibility than voxel grids and enables to better leverage
correlations than per-point classification. On the challenging S3D data we
observe a simple classifier based on the OPlanes representation to yield
compelling results, especially in difficult situations with partial occlusions
due to other objects and partial visibility, which haven't been addressed by
prior work
Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads
With the rapid development of deep learning, object detection and tracking
play a vital role in today's society. Being able to identify and track all the
pedestrians in the dense crowd scene with computer vision approaches is a
typical challenge in this field, also known as the Multiple Object Tracking
(MOT) challenge. Modern trackers are required to operate on more and more
complicated scenes. According to the MOT20 challenge result, the pedestrian is
4 times denser than the MOT17 challenge. Hence, improving the ability to detect
and track in extremely crowded scenes is the aim of this work. In light of the
occlusion issue with the human body, the heads are usually easier to identify.
In this work, we have designed a joint head and body detector in an anchor-free
style to boost the detection recall and precision performance of pedestrians in
both small and medium sizes. Innovatively, our model does not require
information on the statistical head-body ratio for common pedestrians detection
for training. Instead, the proposed model learns the ratio dynamically. To
verify the effectiveness of the proposed model, we evaluate the model with
extensive experiments on different datasets, including MOT20, Crowdhuman, and
HT21 datasets. As a result, our proposed method significantly improves both the
recall and precision rate on small & medium sized pedestrians and achieves
state-of-the-art results in these challenging datasets.Comment: Accepted at AJCAI 202
Understanding the Impact of Image Quality and Distance of Objects to Object Detection Performance
Deep learning has made great strides for object detection in images. The
detection accuracy and computational cost of object detection depend on the
spatial resolution of an image, which may be constrained by both the camera and
storage considerations. Compression is often achieved by reducing either
spatial or amplitude resolution or, at times, both, both of which have
well-known effects on performance. Detection accuracy also depends on the
distance of the object of interest from the camera. Our work examines the
impact of spatial and amplitude resolution, as well as object distance, on
object detection accuracy and computational cost. We develop a
resolution-adaptive variant of YOLOv5 (RA-YOLO), which varies the number of
scales in the feature pyramid and detection head based on the spatial
resolution of the input image. To train and evaluate this new method, we
created a dataset of images with diverse spatial and amplitude resolutions by
combining images from the TJU and Eurocity datasets and generating different
resolutions by applying spatial resizing and compression. We first show that
RA-YOLO achieves a good trade-off between detection accuracy and inference time
over a large range of spatial resolutions. We then evaluate the impact of
spatial and amplitude resolutions on object detection accuracy using the
proposed RA-YOLO model. We demonstrate that the optimal spatial resolution that
leads to the highest detection accuracy depends on the 'tolerated' image size.
We further assess the impact of the distance of an object to the camera on the
detection accuracy and show that higher spatial resolution enables a greater
detection range. These results provide important guidelines for choosing the
image spatial resolution and compression settings predicated on available
bandwidth, storage, desired inference time, and/or desired detection range, in
practical applications
Self-optimization system dynamics simulation of reservoir operating rules
Operating rules have been used widely in the reservoir long-term operation duo to its characteristics of coping with inflow uncertainty and easy implementation. And implicit stochastic optimization (ISO) has been widely applied to derive reservoir operation rules, based on linear regression or nonlinear fitting method. However, the maximum goodness-of-fit criterion of fitting method may be unreliable to determine the effective rules. Therefore, this paper develops a self-optimization system dynamics (SD) simulation of reservoir operation for optimizing the operating rules, by taking advantages of feedback loops in SD simulation. A deterministic optimization operation model is firstly established, and then resolved using dynamic programming (DP). Simultaneously, the initial operating rules (IOR) are derived using the linear fitting method. Finally, the refined optimal operating rules (OOR) are obtained by improving the IOR based on the self-optimization SD simulation. China’s Three Gorges Reservoir is used as a case study. The results show that the SD simulation is competent in simulating a complicated hydropower system with feedback and causal loops. Moreover, it makes a contribution to improve the IOR derived by fitting method within an ISO frame. And the OOR improve effectively the guarantee rate of power generation on the premise of ensuring power generation
Guide RNAs with embedded barcodes boost CRISPR-pooled screens
Abstract We report a new method using re-designed guide RNAs with internal barcodes (iBARs) embedded in their loop regions. Our iBAR approach outperforms the conventional method by producing screening results with much lower false-positive and false-negative rates especially with a high multiplicity of infection (MOI). Importantly, the iBAR approach reduces the starting cells at high MOI significantly with greatly improved efficiency and accuracy compared with the canonical CRISPR screens at a low MOI. This new system is particularly useful when the source of cells is limited or when it is difficult to control viral infection for in vivo screening
Artificial neural network identified the significant genes to distinguish Idiopathic pulmonary fibrosis
Abstract Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease that causes irreversible damage to lung tissue characterized by excessive deposition of extracellular matrix (ECM) and remodeling of lung parenchyma. The current diagnosis of IPF is complex and usually completed by a multidisciplinary team including clinicians, radiologists and pathologists they work together and make decision for an effective treatment, it is imperative to introduce novel practical methods for IPF diagnosis. This study provided a new diagnostic model of idiopathic pulmonary fibrosis based on machine learning. Six genes including CDH3, DIO2, ADAMTS14, HS6ST2, IL13RA2, and IGFL2 were identified based on the differentially expressed genes in IPF patients compare to healthy subjects through a random forest classifier with the existing gene expression databases. An artificial neural network model was constructed for IPF diagnosis based these genes, and this model was validated by the distinctive public datasets with a satisfactory diagnostic accuracy. These six genes identified were significant correlated with lung function, and among them, CDH3 and DIO2 were further determined to be significantly associated with the survival. Putting together, artificial neural network model identified the significant genes to distinguish idiopathic pulmonary fibrosis from healthy people and it is potential for molecular diagnosis of IPF