2,296 research outputs found
Local Features to a Global View: Recognition of Occluded Objects by Spectral Matching Using Pairwise Feature Relationships
Ph.DDOCTOR OF PHILOSOPH
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
Action Recognition Using Visual-Neuron Feature of Motion-Salience Region
This paper proposes a shape-based neurobiological approach for action recognition. Our work is motivated by the successful quantitative model for the organization of the shape pathways in primate visual cortex. In our approach the motion-salience region (MSR) is firstly extracted from the sequential silhouettes of an action. Then, the MSR is represented by simulating the static object representation in the ventral stream of primate visual cortex. Finally, a linear multi-class classifier is used to classify the action. Experiments on publicly available action datasets demonstrate the proposed approach is robust to partial occlusion and deformation of actors and has lower computational cost than the neurobiological models that simulate the motion representation in primate dorsal stream
Stereo Vision and Scene Segmentation
This chapter focuses on how segmentation robustness can be improved by 3D scene geometry
provided by stereo vision systems, as they are simpler and relatively cheaper than most of
current range cameras. In fact, two inexpensive cameras arranged in a rig are often enough
to obtain good results. Another noteworthy characteristic motivating the choice of stereo
systems is that they both provide 3D geometry and color information of the framed scene
without requiring further hardware. Indeed, as it will be seen in following sections, 3D geometry
extraction from a framed scene by a stereo system, also known as stereo reconstruction,
may be eased and improved by scene segmentation since the correspondence research can be
restricted within the same segment in the left and right images
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