100,907 research outputs found
Low power CMOS iImage sensor for face recognition
Imaging system is suitable for different purposes, depending upon their final application. Digital cameras, camcorders, webcams, security cameras or infrared (IR) cameras are well-known imaging systems. For complementary metal oxide semiconductor (CMOS) image sensors, its performance is more promising compared to charged coupled device (CCD) due to its low power consumption, low cost, and on chip functionality and compatibility with standard CMOS technology [1]. In face recognition mode, three dimensional cameras replaced two-dimensional images to measure the object size, distance and shape with time-of-flight (TOF) cameras. Depth information can be extracted to be compared with database [2]. Therefore, CMOS image sensor with its architecture on pixel array, signal processors, row and column selector and timing control [3] will be the main key
DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation
There is an undeniable communication barrier between deaf people and people
with normal hearing ability. Although innovations in sign language translation
technology aim to tear down this communication barrier, the majority of
existing sign language translation systems are either intrusive or constrained
by resolution or ambient lighting conditions. Moreover, these existing systems
can only perform single-sign ASL translation rather than sentence-level
translation, making them much less useful in daily-life communication
scenarios. In this work, we fill this critical gap by presenting DeepASL, a
transformative deep learning-based sign language translation technology that
enables ubiquitous and non-intrusive American Sign Language (ASL) translation
at both word and sentence levels. DeepASL uses infrared light as its sensing
mechanism to non-intrusively capture the ASL signs. It incorporates a novel
hierarchical bidirectional deep recurrent neural network (HB-RNN) and a
probabilistic framework based on Connectionist Temporal Classification (CTC)
for word-level and sentence-level ASL translation respectively. To evaluate its
performance, we have collected 7,306 samples from 11 participants, covering 56
commonly used ASL words and 100 ASL sentences. DeepASL achieves an average
94.5% word-level translation accuracy and an average 8.2% word error rate on
translating unseen ASL sentences. Given its promising performance, we believe
DeepASL represents a significant step towards breaking the communication
barrier between deaf people and hearing majority, and thus has the significant
potential to fundamentally change deaf people's lives
A multi-viewpoint feature-based re-identification system driven by skeleton keypoints
Thanks to the increasing popularity of 3D sensors, robotic vision has experienced huge improvements in a wide range of applications and systems in the last years. Besides the many benefits, this migration caused some incompatibilities with those systems that cannot be based on range sensors, like intelligent video surveillance systems, since the two kinds of sensor data lead to different representations of people and objects. This work goes in the direction of bridging the gap, and presents a novel re-identification system that takes advantage of multiple video flows in order to enhance the performance of a skeletal tracking algorithm, which is in turn exploited for driving the re-identification. A new, geometry-based method for joining together the detections provided by the skeletal tracker from multiple video flows is introduced, which is capable of dealing with many people in the scene, coping with the errors introduced in each view by the skeletal tracker. Such method has a high degree of generality, and can be applied to any kind of body pose estimation algorithm. The system was tested on a public dataset for video surveillance applications, demonstrating the improvements achieved by the multi-viewpoint approach in the accuracy of both body pose estimation and re-identification. The proposed approach was also compared with a skeletal tracking system working on 3D data: the comparison assessed the good performance level of the multi-viewpoint approach. This means that the lack of the rich information provided by 3D sensors can be compensated by the availability of more than one viewpoint
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
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