624 research outputs found
A novel chaotic time series prediction method and its application to carrier vibration interference attitude prediction of stabilized platform
Aiming at the problems existing in previous chaos time series prediction methods, a novel chaos times series prediction method, which applies modified GM(1, 1) model with optimizing parameters to study evolution laws of phase point L1 norm in reconstructed phase space, is proposed in this paper. Phase space reconstruction theory is used to reconstruct the unobserved phase space for chaotic time series by C-C method, and L1 norm series of phase points can be obtained in the reconstructed phase space. The modified GM(1, 1) model, which is improved by optimizing background value and optimizing original condition, is used to study the change law of phase point L1 norm for forecasting. The measured data from stabilized platform experiment and three traditional chaos time series are applied to evaluate the performance of the proposed model. To test the prediction method, three accuracy evaluation standards are employed here. The empirical results of stabilized platform are encouraging and indicate that the newly proposed method is excellent in prediction of chaos time series of chaos systems
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models
Diffusion models have revolted the field of text-to-image generation
recently. The unique way of fusing text and image information contributes to
their remarkable capability of generating highly text-related images. From
another perspective, these generative models imply clues about the precise
correlation between words and pixels. In this work, a simple but effective
method is proposed to utilize the attention mechanism in the denoising network
of text-to-image diffusion models. Without re-training nor inference-time
optimization, the semantic grounding of phrases can be attained directly. We
evaluate our method on Pascal VOC 2012 and Microsoft COCO 2014 under
weakly-supervised semantic segmentation setting and our method achieves
superior performance to prior methods. In addition, the acquired word-pixel
correlation is found to be generalizable for the learned text embedding of
customized generation methods, requiring only a few modifications. To validate
our discovery, we introduce a new practical task called "personalized referring
image segmentation" with a new dataset. Experiments in various situations
demonstrate the advantages of our method compared to strong baselines on this
task. In summary, our work reveals a novel way to extract the rich multi-modal
knowledge hidden in diffusion models for segmentation
Track-before-detect Algorithm based on Cost-reference Particle Filter Bank for Weak Target Detection
Detecting weak target is an important and challenging problem in many
applications such as radar, sonar etc. However, conventional detection methods
are often ineffective in this case because of low signal-to-noise ratio (SNR).
This paper presents a track-before-detect (TBD) algorithm based on an improved
particle filter, i.e. cost-reference particle filter bank (CRPFB), which turns
the problem of target detection to the problem of two-layer hypothesis testing.
The first layer is implemented by CRPFB for state estimation of possible
target. CRPFB has entirely parallel structure, consisting amounts of
cost-reference particle filters with different hypothesized prior information.
The second layer is to compare a test metric with a given threshold, which is
constructed from the output of the first layer and fits GEV distribution. The
performance of our proposed TBD algorithm and the existed TBD algorithms are
compared according to the experiments on nonlinear frequency modulated (NLFM)
signal detection and tracking. Simulation results show that the proposed TBD
algorithm has better performance than the state-of-the-arts in detection,
tracking, and time efficiency
Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review
Interest point detection is one of the most fundamental and critical problems
in computer vision and image processing. In this paper, we carry out a
comprehensive review on image feature information (IFI) extraction techniques
for interest point detection. To systematically introduce how the existing
interest point detection methods extract IFI from an input image, we propose a
taxonomy of the IFI extraction techniques for interest point detection.
According to this taxonomy, we discuss different types of IFI extraction
techniques for interest point detection. Furthermore, we identify the main
unresolved issues related to the existing IFI extraction techniques for
interest point detection and any interest point detection methods that have not
been discussed before. The existing popular datasets and evaluation standards
are provided and the performances for eighteen state-of-the-art approaches are
evaluated and discussed. Moreover, future research directions on IFI extraction
techniques for interest point detection are elaborated
- …