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Description:
Motion detection results
If you double-click the Switch block so that the signal is connected to the SAD side, the Video Viewer block displays the SAD values, which represent the absolute value of the difference between the current and previous image. When these SAD values exceed a threshold value, the demo highlights the quadrant in red.
Note that the difference image itself may be viewed, in place of the original intensity image, along with the red motion highlighting, which indicates how the SAD metric works.
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Size: 11264 |
Author: radim.rh |
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Description: Watermarking embeds information into a digital signal like
audio, image, or video. Reversible image watermarking can restore the
original image without any distortion after the hidden data is extracted.
In this paper, we present a novel reversible watermarking scheme using
an interpolation technique, which can embed a large amount of covert
data into images with imperceptible modification. Different from previous
watermarking schemes, we utilize the interpolation-error, the difference
between interpolation value and corresponding pixel value
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Size: 225280 |
Author: isclor |
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Description: 以镜头光圈分类 镜头有手动光圈（manual iris）和自动光圈（auto iris）之分，配合摄象机使用，手动光圈镜头适合于亮度不变的应用场合，自动光圈镜头因亮度变更时其光圈亦作自动调整，故适用亮度变化的场合。-This paper describes an end-to-end method for extracting
moving targets from a real-time video stream, classifying
them into predefined categories according to imagebased
properties, and then robustly tracking them. Moving
targets are detected using the pixel wise difference between
consecutive image frames. A classification metric is applied
these targets with a temporal consistency constraint
to classify them into three categories: human, vehicle or
background clutter. Once classified, targets are tracked by
a combinationof temporal differencing and templatematching.
Platform: |
Size: 401408 |
Author: 陈思宇 |
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Description: 一篇关于立体视频的主观评价方法的研究，方法较为新颖，值得学习！-A subjective quality assessment metric for stereoscopic video. The realization is novel and worth learning!
Platform: |
Size: 1964032 |
Author: liuyun |
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Description: 行人重定位算法，识别效果非常好，有源码和文章-Person re-identification is an important technique towards
automatic search of a person’s presence in a surveillance
video. Two fundamental problems are critical for
person re-identification, feature representation and metric
learning. An effective feature representation should be robust
to illumination and viewpoint changes, and a discriminant
metric should be learned to match various person images.
In this paper, we propose an effective feature representation
called Local Maximal Occurrence (LOMO), and
a subspace and metric learning method called Cross-view
Quadratic Discriminant Analysis (XQDA). The LOMO feature
analyzes the horizontal occurrence of local features,
and maximizes the occurrence to make a stable representation
against viewpoint changes. Besides, to handle illumination
variations, we apply the Retinex transform and a
scale invariant texture operator. To learn a discriminant
metric, we propose to learn a discriminant low dimensional
subspace by cross-vi
Platform: |
Size: 1156096 |
Author: homawf |
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