Online Object Recognition : The Studies
These are intriguing studies about Online Object Recognition that are useful to know.
The mystery of human superiority in core visual object recognition
An article about how humans and monkeys perform core visual object recognition showed that humans are better at handling confusion than monkeys. The study also found that the state-of-the-art deep artificial neural networks outperformed the human performance by a significant margin.
The Psychology of Seeing: How We Memory Things
A journal about object recognition showed that when people see a brief glimpse of an object in front of them, they are more likely to remember the object than when they see the object in its entirety. This was found by measuring how often participants were able to identify the object seen in a high-resolution image as well as a low-resolution image.
The Neural Basis ofObject Recognition
An inquiry about how recurrent processes contribute to human-level object recognition has shown that these processes are responsible for achieving human-level performance when dealing with challenging objects. Furthermore, this study has also found how the brain is able to solve these tasks under more difficult conditions.
Deep Learning in Object Recognition
A review about how deep learning can be used in object recognition has been conducted. The study showed that deep learning can be used to recognize objects better than supervised learning.
Detecting and Tracking Objects in Videos
A study about the Object Detection, Tracking, and Recognition module built into an internal camera system has found that it can automatically identify and track objects in video content. The systemulse tracked objects can be found throughout the footage which has been shot by professionals in the fields of armor protection and security.
An evolutionary history of object recognition
A study about automatic object recognition has shown that it is able to identify objects much more quickly than humans. At present, this technology is mainly used for automatic identification in a computer system, such as for identity verification or for remote control. This technology could have a great impact on many areas of life, as it could make it easier for people to identify objects and make them easier to handle.
Detecting Object Shape in Images
A paper about object detection in images is provided. Objects in images can be classified by their shape, color, and by their obscured aspects. A similarity-based aspect graph can be used to detect objects in images.
Detecting Changes in Shape or Color: A Royalty-Free Technique
A review about emerging sensor technology that can detect changes in an objects shape or color has the potential to revolutionize the field of security and detection. The study could help individuals and organizations better identify potential threats, identify targets in real-time, and make easier determinations about innocuous objects. There are many different ways sensors could be used to Detect Changes in Shape or Color, but two main methods used in this study are skin prick tests and dye or light scanning infrared (LIR) security cameras.
Detecting Objects with Multiple Algorithms
An evaluation about various object detection algorithms has been done. These include, R-CNN, Fast R-CNN, Faster R-CNN, and Single Shot detector (SSD). Each algorithm has its own advantages and disadvantages so it was decided which one to use for the project.
adversarial learning for object tracking
A research about attention for object tracking with adversarial learning was conducted. The study used a generative model of GAN which attempts to encode the input of the object appearance into feature representation and decode it into corresponding outputs. The discriminative model is a standard convolutional neural network. In the lower part of the network, there are two branches, one forInput data and one forOutput data. The study found that using an adversarial Layer helps to improve the distractibility of the system. Additionally, training with adversarial reinforcement learning results in a behaviour that is more accurate and responsive when tracking objects in challenging scenarios.