Results 181 to 190 of about 8,610,250 (238)

Aggregation‐Controlled Triplet Exciton Regulation Enables Single‐Emitter Color‐Evolving Afterglow

Advanced Science, EarlyView.
A single‐emitter system based on DTP‐R luminophores enables time‐evolving afterglow through aggregation‐regulated excited‐state dynamics. The coexistence and suppression of delayed fluorescence and phosphorescence are controlled via concentration tuning and host‐guest engineering, revealing the RISC origin of delayed fluorescence and enabling ...
Yu Zhang, Zonghang Liu, Gian Albert Alfani, Xilong Wei, Yue Zhang, Ziyu Cui, Yi Lu, Yixuan Chen, Dan Liu, Lin Lu, Zihao Deng, Shuyu Xue, Parvej Alam, Zheng Zhao, Ben Zhong Tang   +14 more
wiley   +1 more source

Design and Application of Nanozymes: Current Trends and Cutting‐Edge Breakthroughs

Advanced Science, EarlyView.
Kelong Fan, Hui Wei, Xiyun Yan
wiley   +1 more source

Design Rules for Quantitative Control of Intragrain Planar Defects in Halide Perovskites

Advanced Science, EarlyView.
Dose‐budgeted electron microscopy reveals the design rules governing intragrain planar defects in halide perovskites: {112}t ferroelastic twins proliferate with increasing tetragonality and grain size, whereas {111}c twins and stacking faults emerge as the lattice approaches cubic symmetry.
Byeongjun Gil, So Jeong Park, Jin Young Kim, Miyoung Kim   +3 more
wiley   +1 more source

A Reconfigurable Memristive Spiking Neuron Enabling Advanced Neuromorphic Computing

Advanced Science, EarlyView.
Pek Jun Tiw and co‐workers develop a reconfigurable memristive neuron that can exhibit four spiking behaviors with freely tunable spike train parameters. The neuron supports multiplexed spike encoding of multiple pieces of information. It also supports advanced neural network strategies, such as neural heterogeneity and dynamic mode switching ...
Pek Jun Tiw, Yuqi Li, Zhongyuan Li, Qihang Ding, Yuzhe Wang, Jiarong Wang, Yuchao Yang   +6 more
wiley   +1 more source

Rotation Invariant Co-Occurrence Matrix Features

Lecture Notes in Computer Science, 2017
Grey level co-occurrence matrix (GLCM) has been one of the most used texture descriptor. GLCMs continue to be very common and extended in various directions, in order to find the best displacement for co-occurrence extraction and a way to describe this co-occurrence that takes into account variation in orientation.
Lorenzo Putzu, Cecilia Di Ruberto
exaly   +5 more sources

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An Integrated Color and Intensity Co-Occurrence Matrix

Pattern Recognition Letters, 2007
The paper presents a novel approach for representing color and intensity of pixel neighborhoods in an image using a co-occurrence matrix. After analyzing the properties of the HSV color space, suitable weight functions have been suggested for estimating relative contribution of color and gray levels of an image pixel.
Shamik Sural, A Vadivel, A K Majumdar
exaly   +3 more sources

Co-occurrence matrix features for fingerprint verification

2011 IEEE International Conference on Anti-Counterfeiting, Security and Identification, 2011
In this paper, an enhanced image-based fingerprint verification algorithm is presented that improves matching accuracy by overcoming the shortcomings of previous methods due to poor image quality. It reduces multi-spectral noise by enhancing a fingerprint image to accurately and reliably determine a reference point and then extracts a 129 X 129 block ...
Mohammed S. Khalil, Muhammad Khurram Khan, Muhammad Imran Razzak   +2 more
openaire   +2 more sources

Image Retrieval Based on the Texton Co-Occurrence Matrix

Pattern Recognition, 2008
This paper put forward a new method of co-occurrence matrix to describe image features. This method can express the spatial correlation of textons. During the course of feature extracting, we have quantized the original images into 256 colors and computed color gradient from the RGB vector space, and then calculated the statistical information of ...
Guang-Hai Liu, Jing-Yu Yang
exaly   +3 more sources

Temporal co-occurrence matrix approaches to motion analysis

SPIE Proceedings, 2004
We propose a novel method for motion analysis in video sequences. It extends the co-occurrence matrix concept for texture analysis to the temporal domain. The approach proved to be versatile in the sense of targeting different motion analysis tasks.
A. Ukovich, M. Koeppen, RAMPONI, GIOVANNI   +2 more
openaire   +3 more sources

Improving Co-occurrence Matrix Feature Discrimination [PDF]

, 1995
This paper discusses a method of improving the discrimination power of a certain class of GLCM features. We investigate where co-occurrence matrix features derive their discriminatory power, and provide a theoretical basis for improving this method. Finally, we present examples of discrimination improvement using a real-world data.
Walker, Ross F., Jackway, Paul, Longstaff, I. D.   +2 more
core   +2 more sources