1,465 research outputs found
Learning to Hash-tag Videos with Tag2Vec
User-given tags or labels are valuable resources for semantic understanding
of visual media such as images and videos. Recently, a new type of labeling
mechanism known as hash-tags have become increasingly popular on social media
sites. In this paper, we study the problem of generating relevant and useful
hash-tags for short video clips. Traditional data-driven approaches for tag
enrichment and recommendation use direct visual similarity for label transfer
and propagation. We attempt to learn a direct low-cost mapping from video to
hash-tags using a two step training process. We first employ a natural language
processing (NLP) technique, skip-gram models with neural network training to
learn a low-dimensional vector representation of hash-tags (Tag2Vec) using a
corpus of 10 million hash-tags. We then train an embedding function to map
video features to the low-dimensional Tag2vec space. We learn this embedding
for 29 categories of short video clips with hash-tags. A query video without
any tag-information can then be directly mapped to the vector space of tags
using the learned embedding and relevant tags can be found by performing a
simple nearest-neighbor retrieval in the Tag2Vec space. We validate the
relevance of the tags suggested by our system qualitatively and quantitatively
with a user study
Two Decades of Colorization and Decolorization for Images and Videos
Colorization is a computer-aided process, which aims to give color to a gray
image or video. It can be used to enhance black-and-white images, including
black-and-white photos, old-fashioned films, and scientific imaging results. On
the contrary, decolorization is to convert a color image or video into a
grayscale one. A grayscale image or video refers to an image or video with only
brightness information without color information. It is the basis of some
downstream image processing applications such as pattern recognition, image
segmentation, and image enhancement. Different from image decolorization, video
decolorization should not only consider the image contrast preservation in each
video frame, but also respect the temporal and spatial consistency between
video frames. Researchers were devoted to develop decolorization methods by
balancing spatial-temporal consistency and algorithm efficiency. With the
prevalance of the digital cameras and mobile phones, image and video
colorization and decolorization have been paid more and more attention by
researchers. This paper gives an overview of the progress of image and video
colorization and decolorization methods in the last two decades.Comment: 12 pages, 19 figure
Fusion based Image Enhancement Approach for Brain Tumor Detection
Magnetic Resonance Imaging (MRI), is a crucial technology used in the processing of medical images that provides insights into the anatomy of soft organs in the human body and helps in detecting brain tumors and spinal tumors. Despite advances in technology, most images have intrinsic drawbacks such as reduced contrast and brightness, and noise. Several contrast enhancement techniques are used such as, HE, BBHE, DSIHE, CLAHE, RMSHE, and their fusion, have been deployed on different MRI images to handle these problems. Metrics such as, entropy, PIQE and BRISQUE are used in the assessment of the results. Through the different fusion combinations, most prominent results are obtained from CLAHE-RMSHE fusion with an entropy value of 6.2516 and BRISQUE value of 40.14
Recent Trends in Computational Intelligence
Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications
Quantifying and improving laser range data when scanning industrial materials
This paper presents the procedure and results of a performance study of a miniature laser range scanner, along with a novel error correction calibration. Critically, the study investigates the accuracy and performance of the ranger sensor when scanning large industrial materials over a range of distances. Additionally, the study investigated the effects of small orientation angle changes of the scanner, in a similar manner to which it would experience when being deployed on a mobile robotic platform. A detailed process of error measurement and visualisation was undertaken on a number of parameters, not limited to traditional range data but also received intensity and amplifier gain. This work highlights that significant range distance errors are introduced when optically laser scanning common industrial materials, such as aluminum and stainless steel. The specular reflective nature of some materials results in large deviation in range data from the true value, with mean RMSE errors as high as 100.12 mm recorded. The correction algorithm was shown to reduce the RMSE error associated with range estimation on a planar aluminium surface from 6.48% to 1.39% of the true distance range
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