3,740 research outputs found
Activity profiling for minimally invasive surgery
Imperial Users onl
Moving cast shadows detection methods for video surveillance applications
Moving cast shadows are a major concern in today’s performance from broad range of many vision-based surveillance applications because they highly difficult the object classification task. Several shadow detection methods have been reported in the literature during the last years. They are mainly divided into two domains. One usually works with static images, whereas the second one uses image sequences, namely video content. In spite of the fact that both cases can be analogously analyzed, there is a difference in the application field. The first case, shadow detection methods can be exploited in order to obtain additional geometric and semantic cues about shape and position of its casting object (’shape from shadows’) as well as the localization of the light source. While in the second one, the main purpose is usually change detection, scene matching or surveillance (usually in a background subtraction context). Shadows can in fact modify in a negative way the shape and color of the target object and therefore affect the performance of scene analysis and interpretation in many applications. This chapter wills mainly reviews shadow detection methods as well as their taxonomies related with the second case, thus aiming at those shadows which are associated with moving objects (moving shadows).Peer Reviewe
Separating Reflection and Transmission Images in the Wild
The reflections caused by common semi-reflectors, such as glass windows, can
impact the performance of computer vision algorithms. State-of-the-art methods
can remove reflections on synthetic data and in controlled scenarios. However,
they are based on strong assumptions and do not generalize well to real-world
images. Contrary to a common misconception, real-world images are challenging
even when polarization information is used. We present a deep learning approach
to separate the reflected and the transmitted components of the recorded
irradiance, which explicitly uses the polarization properties of light. To
train it, we introduce an accurate synthetic data generation pipeline, which
simulates realistic reflections, including those generated by curved and
non-ideal surfaces, non-static scenes, and high-dynamic-range scenes.Comment: accepted at ECCV 201
Robust Reflection Removal with Flash-only Cues in the Wild
We propose a simple yet effective reflection-free cue for robust reflection
removal from a pair of flash and ambient (no-flash) images. The reflection-free
cue exploits a flash-only image obtained by subtracting the ambient image from
the corresponding flash image in raw data space. The flash-only image is
equivalent to an image taken in a dark environment with only a flash on. This
flash-only image is visually reflection-free and thus can provide robust cues
to infer the reflection in the ambient image. Since the flash-only image
usually has artifacts, we further propose a dedicated model that not only
utilizes the reflection-free cue but also avoids introducing artifacts, which
helps accurately estimate reflection and transmission. Our experiments on
real-world images with various types of reflection demonstrate the
effectiveness of our model with reflection-free flash-only cues: our model
outperforms state-of-the-art reflection removal approaches by more than 5.23dB
in PSNR. We extend our approach to handheld photography to address the
misalignment between the flash and no-flash pair. With misaligned training data
and the alignment module, our aligned model outperforms our previous version by
more than 3.19dB in PSNR on a misaligned dataset. We also study using linear
RGB images as training data. Our source code and dataset are publicly available
at https://github.com/ChenyangLEI/flash-reflection-removal.Comment: Extension of CVPR 2021 paper [arXiv:2103.04273], submitted to TPAMI.
Our source code and dataset are publicly available at
http://github.com/ChenyangLEI/flash-reflection-remova
Effect of cooking time on physical properties of almond milk-based lemak cili api gravy
One of the crucial elements in developing or reformulating product is to maintain the quality throughout its entire shelf life. This study aims to determine the effect of different cooking time on the almond milk-based of lemak cili api gravy. Various cooking times of 5, 10, 15, 20, 25 and 30 minutes were employed to the almond milk-based lemak cili api gravy followed by determination of their effects on physical properties such as total soluble solids content, pH and colour. pH was determined by using a pH meter. Refractometer was used to evaluate the total soluble solids content of almond milk-based lemak cili api gravy. The colours were determined by using spectrophotometer which expressed as L*, a* and b* values. Results showed that almond milk-based lemak cili api gravy has constant values of total soluble solids with pH range of 5 to 6, which can be classified as low acid food. Colour analysis showed that the lightness (L*) and yellowness (b*) are significantly increased while redness (a*) decreased. In conclusion, this study shows that physical properties of almond milk-based lemak cili api gravy changes by increasing the cooking time
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