4 research outputs found
Using Prior Knowledge for Verification and Elimination of Stationary and Variable Objects in Real-time Images
With the evolving technologies in the autonomous vehicle industry, now it has become possible for automobile passengers to sit relaxed instead of driving the car. Technologies like object detection, object identification, and image segmentation have enabled an autonomous car to identify and detect an object on the road in order to drive safely. While an autonomous car drives by itself on the road, the types of objects surrounding the car can be dynamic (e.g., cars and pedestrians), stationary (e.g., buildings and benches), and variable (e.g., trees) depending on if the location or shape of an object changes or not. Different from the existing image-based approaches to detect and recognize objects in the scene, in this research 3D virtual world is employed to verify and eliminate stationary and variable objects to allow the autonomous car to focus on dynamic objects that may cause danger to its driving. This methodology takes advantage of prior knowledge of stationary and variable objects presented in a virtual city and verifies their existence in a real-time scene by matching keypoints between the virtual and real objects. In case of a stationary or variable object that does not exist in the virtual world due to incomplete pre-existing information, this method uses machine learning for object detection. Verified objects are then removed from the real-time image with a combined algorithm using contour detection and class activation map (CAM), which helps to enhance the efficiency and accuracy when recognizing moving objects
Deep learning in food category recognition
Integrating artificial intelligence with food category recognition has been a field of interest for research for the
past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The
modern advent of big data and the development of data-oriented fields like deep learning have provided advancements
in food category recognition. With increasing computational power and ever-larger food datasets,
the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied
to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We
survey the core components for constructing a machine learning system for food category recognition, including
datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a
particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer
learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments
in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial
Fund (RP202G0289)LIAS (P202ED10Data Science
Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK
Education Fund (OP202006)BBSRC (RM32G0178B8
Sensors for Vital Signs Monitoring
Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data
Vehicle Verification Between Two Nonoverlapped Views Using Sparse Representation
[[abstract]]Vehicle verification in different scenes is a nontrivial problem that cannot be solved by simple correspondence matching. In the paper, the verification problem is treated as a binary classification problem. If the two vehicles in two views are the same, they are a positive pair; otherwise, a negative pair. Here, we propose an effective sparse representation (SR) method called Boost K-SVD to generate the feature vectors for vehicle representation. In Boost K-SVD, the particle filtering is first applied for the initial atom selection. Then, it finds the atoms satisfying the restricted isometry property (RIP). Finally, we propose a discrimination criterion to determine the optimal dictionary size. Instead of initial random atom selection, Boost K-SVD generates the atoms incrementally to create a more compact dictionary. Furthermore, the dictionary with RIP can produce sparser representation vectors with higher verification accuracy. Experimental results show that our method has better performance compared with the other methods