Light field image quality assessment (LF-IQA) plays a significant role due to its guidance to Light Field (LF) contents acquisition, processing and application. The LF can be represented as 4-D signal, and its quality...
Light field image quality assessment (LF-IQA) plays a significant role due to its guidance to Light Field (LF) contents acquisition, processing and application. The LF can be represented as 4-D signal, and its quality depends on both angular consistency and spatial quality. However, few existing LF-IQA methods concentrate on effects caused by angular inconsistency. Especially, no-reference methods lack effective utilization of 2D angular information. In this paper, we focus on measuring the 2-D angular consistency for LF-IQA. The Micro-Lens Image (MLI) refers to the angular domain of the LF image, which can simultaneously record the angular information in both horizontal and vertical directions. Since the MLI contains 2D angular information, we propose a No-Reference Light Field image Quality assessment model based on MLI (LF-QMLI). Specifically, we first utilize Global Entropy Distribution (GED) and Uniform Local Binary Pattern descriptor (ULBP) to extract features from the MLI, and then pool them together to measure angular consistency. In addition, the information entropy of SubAperture Image (SAI) is adopted to measure spatial quality. Extensive experimental results show that LF-QMLI achieves the state-of-the-art performance.
Semantic segmentation is a fundamental task in indoor scene understanding. Most previous supervised approaches rely on densely annotated image data sets. Due to the limited amount of images with segmentation labels, t...
ISBN:
(数字)9781728123455
ISBN:
(纸本)9781728123462
Semantic segmentation is a fundamental task in indoor scene understanding. Most previous supervised approaches rely on densely annotated image data sets. Due to the limited amount of images with segmentation labels, the performance of existing networks is greatly limited. In this paper, we exploit temporal correlation in video frames to improve the performance and robustness of segmentation networks. Two effective learning strategies are proposed to propagate the information from a few labeled frames to their immediate neighbor frames. First, we scale up training dataset for supervised semantic segmentation networks by generating pseudo ground-truth for neighboring frames from a labeled frame using filtered homography transformation. Furthermore, we introduce a self-supervised loss function to ensure temporal consistency between the segmentation results of adjacent frames. The experimental results demonstrate that our proposed method outperforms state-of-the-art techniques for semantic segmentation on NYU-Depth V2 dataset.
The following topics are dealt with: video coding; data compression; image coding; convolutional neural nets; decoding; learning (artificial intelligence); motion compensation; video codecs; image reconstruction; filt...
The following topics are dealt with: video coding; data compression; image coding; convolutional neural nets; decoding; learning (artificial intelligence); motion compensation; video codecs; image reconstruction; filtering theory.
Interferometric synthetic aperture radar (InSAR) can be used to extract digital elevation model (DEM) with high accuracy. However, the side looking geometry of synthetic aperture radar (SAR) may cause geometric distor...
详细信息
Automatic color enhancement is aimed to adaptively adjust photos to expected styles and tones. For current learned methods in this field, global harmonious perception and local details are hard to be well-considered i...
详细信息
Light field image quality assessment (LF-IQA) plays a significant role due to its guidance to Light Field (LF) contents acquisition, processing and application. The LF can be represented as 4-D signal, and its quality...
详细信息
Convolutional neural networks (CNNs) are powerful and have achieved state-of-the-art performance in many visual recognition tasks. Despite their impressive performance, CNNs are still unable to remain invariant while ...
详细信息
Convolutional neural networks (CNNs) are powerful and have achieved state-of-the-art performance in many visual recognition tasks. Despite their impressive performance, CNNs are still unable to remain invariant while some spatial transformations are applied on images. Herein, we propose representation-consistent neural networks to solve this problem. By introducing consistent losses between the representations in different layers of transformed images, the recognition performance of transformed images is significantly improved. This model not only learns to map from the transformed images to the pre-defined labels but each layer also learns to generate invariant representations when the input images are transformed. All the characteristics of transformation invariance are embedded in the model, which means that no extra parameters or computations are introduced in the well-trained model. Comparative experiments demonstrate the superiority of our model when learning invariance to rotation, translation, and scaling on large-scale image recognition and retrieval tasks.
Imaging spectroscopy has become a pivotal technique for estimating plant traits at the canopy scale. Accurate trait prediction is critical for biodiversity conservation, yet research on canopy traits in heterogeneous ...
详细信息
Imaging spectroscopy has become a pivotal technique for estimating plant traits at the canopy scale. Accurate trait prediction is critical for biodiversity conservation, yet research on canopy traits in heterogeneous wetlands with complex species mixtures remains scarce. While the Community-Weighted Mean (CWM) method has been widely used for upscaling leaf traits to the canopy level, it often suffers from low model precision, and the suitability of alternative upscaling methods for predicting canopy mean traits using imaging spectroscopy remains uncertain. This study proposed a novel approach for calculating canopy mean traits using the geometric mean method and compared its performance to that of the CWM methods in combination with three modeling algorithms Partial Least Squares Regression (PLSR), Random Forest regression (RF), and Support Vector Machine regression (SVM). The accuracy was evaluated by exploring the predictive ability for nine canopy mean traits by using high spatial resolution UAV multispectral data. The analysis focuses on a wetland ecosystem characterized by high species diversity and hydrological variability, where precise plant trait estimation is essential for ecological process modeling. The results demonstrated that the geometric mean method yielded the highest validation accuracy for most canopy mean traits when paired with the SVM model (e.g., R 2 for N = 0.64, SLA = 0.38, and cellulose = 0.33). Notably, the geometric mean method, combined with UAV multispectral data, significantly enhanced the predictive performance for N, surpassing even that of hyperspectral data. This study underscores the potential of the geometric mean method for upscaling leaf traits to canopy traits. These findings contribute to advancing the prediction accuracy of plant functional traits through remote sensing techniques, while future studies may explore the integration of deep learning methods.
In High Efficiency Video Coding (HEVC), excellent rate-distortion (RD) performance is achieved in part by having a flexible quadtree coding unit (CU) partition and a large number of intra-prediction modes. Such an exc...
详细信息
Synthetic aperture radar (SAR) has a good ability to detect the microwave scattering characteristics of the target and has a good capability of slant range Doppler positioning. Using multi-view SAR images in combinati...
详细信息
暂无评论