364 research outputs found
PointNorm: Dual Normalization is All You Need for Point Cloud Analysis
Point cloud analysis is challenging due to the irregularity of the point
cloud data structure. Existing works typically employ the ad-hoc
sampling-grouping operation of PointNet++, followed by sophisticated local
and/or global feature extractors for leveraging the 3D geometry of the point
cloud. Unfortunately, the sampling-grouping operations do not address the point
cloud's irregularity, whereas the intricate local and/or global feature
extractors led to poor computational efficiency. In this paper, we introduce a
novel DualNorm module after the sampling-grouping operation to effectively and
efficiently address the irregularity issue. The DualNorm module consists of
Point Normalization, which normalizes the grouped points to the sampled points,
and Reverse Point Normalization, which normalizes the sampled points to the
grouped points. The proposed framework, PointNorm, utilizes local mean and
global standard deviation to benefit from both local and global features while
maintaining a faithful inference speed. Experiments show that we achieved
excellent accuracy and efficiency on ModelNet40 classification, ScanObjectNN
classification, ShapeNetPart Part Segmentation, and S3DIS Semantic
Segmentation. Code is available at
https://github.com/ShenZheng2000/PointNorm-for-Point-Cloud-Analysis
StrokeStyles: Stroke-based Segmentation and Stylization of Fonts
We develop a method to automatically segment a font’s glyphs into a set of overlapping and intersecting strokes with the aim of generating artistic stylizations. The segmentation method relies on a geometric analysis of the glyph’s outline, its interior, and the surrounding areas and is grounded in perceptually informed principles and measures. Our method does not require training data or templates and applies to glyphs in a large variety of input languages, writing systems, and styles. It uses the medial axis, curvilinear shape features that specify convex and concave outline parts, links that connect concavities, and seven junction types. We show that the resulting decomposition in strokes can be used to create variations, stylizations, and animations in different artistic or design-oriented styles while remaining recognizably similar to the input font
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
How Brand Names Brand Societies: A Comparative Study of Brand Names Registered in Selected English-Speaking Countries 1870-1980
Objectives were to investigate the registered brand name system in selected English-speaking countries, to determine attributes of brand names ( brands ) and whether brand attributes characterize their source countries. Officials in Australia, Canada, India, Ireland, Kenya, the United Kingdom, and the United States provided data routinely recorded in registering brand names--identified by random numbers preselected by this author. Each brand, whether only verbal, or only design, or mixed verbal/design, was coded for several dozen characteristics: general, morphological, goods-related, and meaning-related, including, for each, official numbers and dates, registering or renewing entity, goods so branded, and any goods-related meaning. Included, if verbal, were initial letters and word length; and, if design, whether abstract or pictorial, and type if pictorial. Brand names were characterized as a long-continuing mass communication symbol system. Textile brands are omnipresent, but in the developing countries medical (and sometimes cosmetic and/or leisure) brands are more frequent than brands for the biblical necessities of food, clothing, and shelter--which predominate in the industrialized countries. Over time, brand verbal content has increased whereas embellishment, as in use of borders and overt design content, has decreased markedly. India ranks highest in purely design and mixed verbal/design brands, and Ireland ranks highest in purely verbal, lowest in mixed verbal/design, brands. Recent years show modest resurgence in registration of designs--more in brand names with verbal content than in pure designs. Yet mixed verbal/design brands, possibly expected to survive better than do purely verbal or purely design brands, are less likely to be renewed. Renewal of registration was selected as a survival measure of success. Brands with trivial ( arbitrary ) meaning or excessive ( descriptive ) meaning about the branded goods survived better than intermediate ( suggestive ) ones. Source countries were characterized according to their brand name features--and were found to cluster together, or to diverge from one or more others, depending upon feature(s) selected
PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models
Generalizable 3D part segmentation is important but challenging in vision and
robotics. Training deep models via conventional supervised methods requires
large-scale 3D datasets with fine-grained part annotations, which are costly to
collect. This paper explores an alternative way for low-shot part segmentation
of 3D point clouds by leveraging a pretrained image-language model, GLIP, which
achieves superior performance on open-vocabulary 2D detection. We transfer the
rich knowledge from 2D to 3D through GLIP-based part detection on point cloud
rendering and a novel 2D-to-3D label lifting algorithm. We also utilize
multi-view 3D priors and few-shot prompt tuning to boost performance
significantly. Extensive evaluation on PartNet and PartNet-Mobility datasets
shows that our method enables excellent zero-shot 3D part segmentation. Our
few-shot version not only outperforms existing few-shot approaches by a large
margin but also achieves highly competitive results compared to the fully
supervised counterpart. Furthermore, we demonstrate that our method can be
directly applied to iPhone-scanned point clouds without significant domain
gaps.Comment: CVPR 2023, project page: https://colin97.github.io/PartSLIP_page
An Integrated Enhancement Solution for 24-hour Colorful Imaging
The current industry practice for 24-hour outdoor imaging is to use a silicon
camera supplemented with near-infrared (NIR) illumination. This will result in
color images with poor contrast at daytime and absence of chrominance at
nighttime. For this dilemma, all existing solutions try to capture RGB and NIR
images separately. However, they need additional hardware support and suffer
from various drawbacks, including short service life, high price, specific
usage scenario, etc. In this paper, we propose a novel and integrated
enhancement solution that produces clear color images, whether at abundant
sunlight daytime or extremely low-light nighttime. Our key idea is to separate
the VIS and NIR information from mixed signals, and enhance the VIS signal
adaptively with the NIR signal as assistance. To this end, we build an optical
system to collect a new VIS-NIR-MIX dataset and present a physically meaningful
image processing algorithm based on CNN. Extensive experiments show outstanding
results, which demonstrate the effectiveness of our solution.Comment: AAAI 2020 (Oral
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