Playing Tic-Tac-Toe Games with Intelligent Single-pixel Imaging
- URL: http://arxiv.org/abs/2205.03663v1
- Date: Sat, 7 May 2022 14:45:54 GMT
- Title: Playing Tic-Tac-Toe Games with Intelligent Single-pixel Imaging
- Authors: Shuming Jiao, Jiaxiang Li, Wei Huang, Zibang Zhang
- Abstract summary: Single-pixel imaging (SPI) is a novel optical imaging technique by replacing a two-dimensional pixelated sensor with a single-pixel detector and pattern illuminations.
In this work, a novel non-image-based task of playing Tic-Tac-Toe games interactively is merged into the framework of SPI.
- Score: 4.363127731705663
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Single-pixel imaging (SPI) is a novel optical imaging technique by replacing
a two-dimensional pixelated sensor with a single-pixel detector and pattern
illuminations. SPI have been extensively used for various tasks related to
image acquisition and processing. In this work, a novel non-image-based task of
playing Tic-Tac-Toe games interactively is merged into the framework of SPI. An
optoelectronic artificial intelligent (AI) player with minimal digital
computation can detect the game states, generate optimal moves and display
output results mainly by pattern illumination and single-pixel detection.
Simulated and experimental results demonstrate the feasibility of proposed
scheme and its unbeatable performance against human players.
Related papers
- Parameter-Inverted Image Pyramid Networks [49.35689698870247]
We propose a novel network architecture known as the Inverted Image Pyramid Networks (PIIP)
Our core idea is to use models with different parameter sizes to process different resolution levels of the image pyramid.
PIIP achieves superior performance in tasks such as object detection, segmentation, and image classification.
arXiv Detail & Related papers (2024-06-06T17:59:10Z) - Dual-Scale Transformer for Large-Scale Single-Pixel Imaging [11.064806978728457]
We propose a deep unfolding network with hybrid-attention Transformer on Kronecker SPI model, dubbed HATNet, to improve the imaging quality of real SPI cameras.
The gradient descent module can avoid high computational overheads rooted in previous gradient descent modules based on vectorized SPI.
The denoising module is an encoder-decoder architecture powered by dual-scale spatial attention for high- and low-frequency aggregation and channel attention for global information recalibration.
arXiv Detail & Related papers (2024-04-07T15:53:21Z) - MMPI: a Flexible Radiance Field Representation by Multiple Multi-plane
Images Blending [61.45757368117578]
This paper presents a flexible representation of neural radiance fields based on multi-plane images (MPI)
MPI is widely used in NeRF learning for its simple definition, easy calculation, and powerful ability to represent unbounded scenes.
We show that MPI can synthesize high-quality novel views of complex scenes with diverse camera distributions and view directions.
arXiv Detail & Related papers (2023-09-30T04:36:43Z) - Pixel-Inconsistency Modeling for Image Manipulation Localization [63.54342601757723]
Digital image forensics plays a crucial role in image authentication and manipulation localization.
This paper presents a generalized and robust manipulation localization model through the analysis of pixel inconsistency artifacts.
Experiments show that our method successfully extracts inherent pixel-inconsistency forgery fingerprints.
arXiv Detail & Related papers (2023-09-30T02:54:51Z) - PS-Transformer: Learning Sparse Photometric Stereo Network using
Self-Attention Mechanism [4.822598110892846]
Existing deep calibrated photometric stereo networks aggregate observations under different lights based on pre-defined operations such as linear projection and max pooling.
To tackle this issue, this paper presents a deep sparse calibrated photometric stereo network named it PS-Transformer which leverages the learnable self-attention mechanism to properly capture the complex inter-image interactions.
arXiv Detail & Related papers (2022-11-21T11:58:25Z) - Solving combinational optimization problems with evolutionary
single-pixel imaging [4.363127731705663]
Single-pixel imaging (SPI) is a novel optical imaging technique by replacing the pixelated sensor array in a conventional camera with a single-pixel detector.
In this work, we propose a SPI scheme for processing other types of data in addition to images.
arXiv Detail & Related papers (2022-10-12T05:06:31Z) - Multitask AET with Orthogonal Tangent Regularity for Dark Object
Detection [84.52197307286681]
We propose a novel multitask auto encoding transformation (MAET) model to enhance object detection in a dark environment.
In a self-supervision manner, the MAET learns the intrinsic visual structure by encoding and decoding the realistic illumination-degrading transformation.
We have achieved the state-of-the-art performance using synthetic and real-world datasets.
arXiv Detail & Related papers (2022-05-06T16:27:14Z) - SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and
Few-Shot Detection Problems [5.668569695717809]
Deep neural networks (DNNs) have achieved tremendous success for object detection in overhead (e.g., satellite) imagery.
One ongoing challenge is the acquisition of training data, due to high costs of obtaining satellite imagery and annotating objects in it.
We present a simple approach - termed Synthetic object IMPLantation (SIMPL) - to easily and rapidly generate large quantities of synthetic overhead training data for custom target objects.
arXiv Detail & Related papers (2021-06-29T19:06:05Z) - Interaction-free imaging of multi-pixel objects [58.720142291102135]
Quantum imaging is well-suited to study sensitive samples which require low-light conditions, like biological tissues.
In this context, interaction-free measurements (IFM) allow us infer the presence of an opaque object without the photon interacting with the sample.
Here we extend the IFM imaging schemes to multi-pixel, semi-transparent objects, by encoding the information about the pixels into an internal degree of freedom.
arXiv Detail & Related papers (2021-06-08T06:49:19Z) - Physics-based Differentiable Depth Sensor Simulation [5.134435281973137]
We introduce a novel end-to-end differentiable simulation pipeline for the generation of realistic 2.5D scans.
Each module can be differentiated w.r.t sensor and scene parameters.
Our simulation greatly improves the performance of the resulting models on real scans.
arXiv Detail & Related papers (2021-03-30T17:59:43Z) - Two-shot Spatially-varying BRDF and Shape Estimation [89.29020624201708]
We propose a novel deep learning architecture with a stage-wise estimation of shape and SVBRDF.
We create a large-scale synthetic training dataset with domain-randomized geometry and realistic materials.
Experiments on both synthetic and real-world datasets show that our network trained on a synthetic dataset can generalize well to real-world images.
arXiv Detail & Related papers (2020-04-01T12:56:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.