Showing papers in "Journal of Visual Communication and Image Representation in 2019"

TL;DR: A convolutional neural network called SphereReID is proposed adopting Sphere Softmax and training a single model end-to-end with a new warming-up learning rate schedule on four challenging datasets including Market-1501, DukeMTMC-reID, CHHK-03, and CUHK-SYSU.

TL;DR: In this paper, the authors proposed an interpretable feedforward (FF) design without any backpropagation, which adopts a data-centric approach to derive network parameters of the current layer based on data statistics from the output of the previous layer in a one-pass manner.

TL;DR: This survey provides an overview on typical image tampering types, released image tampering datasets and recent tampering detection approaches and encourages the research community to develop general tampering localization methods in the future instead of adhering to single-type tampering detection.

TL;DR: A method of image-to-image translation, CTGAN (Multi-Camera Transfer GAN), which can be performed on multiple camera domains of pedestrian dataset by using one single model, and adopts the MSCDA (Mixed Selective Convolution Descriptor Aggregation) method, which can locate the main pedestrian objects in the image, filter out the background noise, and keep the useful depth descriptor.

TL;DR: This paper optimizes the PCA algorithm for dimension reduction of image feature extraction by deep learning, aiming at the problem that it is difficult to process high-dimensional sparse big data based on PCA algorithms.

TL;DR: In this paper, the combination of isolated facial components and a contextual feature called foggy face is used to train deep convolutional neural networks followed by an AdaBoost-based score fusion to infer the final gender class.

TL;DR: An effective face recognition model based on principal component analysis, genetic algorithm and support vector machine is established, in which principal components analysis is used to reduce feature dimension, genetic algorithms are used to optimize search strategy, and support vectors machine isUsed to realize classification.

TL;DR: Experiments on the AFEW, CK+ and MMI datasets using subject-independent validation scheme demonstrate that the integrated framework of two networks achieves a better performance than using individual network separately, compared with state-of-the-arts methods.

TL;DR: This work proposes a novel framework to embed a scene graph into the structural representation, which captures the semantic concepts and the graph topology and develops the scene-graph-driven method to generate the attention graph.

TL;DR: An integrated approach for enhancing design ideation by applying artificial intelligence and data mining techniques, which consists of two models, a semantic ideation network and a visual concepts combination model, which provide inspiration semantically and visually based on computational creativity theory.

TL;DR: The experimental results prove that the proposed novel reversible data hiding method can reach a high embedding rate and a high PSNR.

TL;DR: Experimental results demonstrate that TRS-DBN has higher change detection accuracy than similar methods and a good automation level.

TL;DR: Experimental results on a real retinal OCT dataset and a musculoskeletal radiographs dataset demonstrate the superiority of the proposed convolutional neural network method over the traditional CNN and several well-known OCT classification methods.

TL;DR: A two-pathway model with average and max pooling layers in different paths is designed and combined with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results.

TL;DR: The PFC∼2D numerical calculation model of soil-rock mixtures is established and the results show that when the stone content is 80%, the analysis should be caused by the large amount of rock, which leads to the large internal voids, and the sudden unloading between the rock and the rock during compaction and then the structural reorganization.

TL;DR: Experimental results prove the superiority of the proposed technique over existing state-of-the-art methods in terms of both subjective and objective evaluation.

TL;DR: A computer vision based framework is proposed that detects falls from surveillance videos by employing background subtraction and rank pooling to model spatial and temporal representations in videos, and introducing a novel three-stream Convolutional Neural Networks as an event classifier.

TL;DR: Under Gaussianity and linearity assumptions, the existing Gaussian mixture implementation of the standard PHD filter is extended to create a N-type GM-PHD filter, and Munkres's variant of the Hungarian assignment algorithm is used to associate tracked target identities between frames.

TL;DR: Three different methods namely Band Reordering based on Consecutive Continuity Breakdown Heuristics (BRCCBH), Band Re ordering based on Weighted-Correlation Heuristic (BRWCH) and Segmented BRCCBh have been proposed for the compression of multispectral, hyperspectral and hyperspectrals sounder data.

TL;DR: Charbonnier and L1 loss functions are fastest ones when the computational time cost is examined during training stage, and both are sensitive to noise that misleads the learning process and consequently resulting in lower quality HR outcomes.

TL;DR: The algorithm presented in this article can well embed the color image in the carrier image, and has good resistance to attack operations such as loss compression and adding of noise.

TL;DR: A new end-to-end depth-aware saliency model using three convolutional neural networks including color saliency network, depth Saliency network and saliency fusion network, for saliency detection in RGBD images and stereoscopic images is proposed.

TL;DR: This work proposes an event summarization technique using Deep learning framework for monocular videos that outperforms the state-of-the-art models on Precision and F-measure and also cover the major contents of the original video.

TL;DR: A weighted edge-based level set method based on multi-local statistical information to better segment noisy images and provides higher segmentation accuracies and more accurate segmentation results, which demonstrate its effectiveness and robustness.

TL;DR: A novel post-processing method based on GAN (Generative Adversarial Network) is explored to reinforce spatial contiguity in the output label maps to get better performance and stability.

TL;DR: The results show that the scanning voltage and filtering function have great influence on CT images and CT numbers of rock and soil samples, and with the help of reasonable CT scanning parameters, the quality of the geotechnical CT image can be improved and the relatively accurate geotehnical CT value can be obtained.

TL;DR: A user study comparing remote collaboration with an interface that combined hand gestures and sketching to one that only used hand gestures, when solving two tasks; Lego assembly and repairing a laptop found that adding sketch cues improved the task completion time, only with the repairing task.

TL;DR: A review of crowd behavior analysis methods including Gaussian Mixture Model (GMM), Hidden Markov Model (HMM), Optical Flow method and Spatio-Temporal Technique (STT) to provide insight on several detection methods.

TL;DR: The results show that ImmerTai can accelerate the learning process of students noticeably compared to non-immersive learning with the conventional PC setup, and there is a substantial difference in the quality of the learnt motion between CAVE and HMD compared to PC.

TL;DR: The research addresses that point for content based image retrieval (CBIR) by fusing parametric color and shape features with nonparametric texture feature to propose a robust and effective algorithm.