HDF-Net: Capturing Homogeny Difference Features to Localize the Tampered Image.

Modern image editing software enables anyone to alter the content of an image to deceive the public, which can pose a security hazard to personal privacy and public safety. The detection and localization of image tampering is becoming an urgent issue to be addressed. We have revealed that the tampered region exhibits homogenous differences (the changes in metadata organization form and organization structure of the image) from the real region after manipulations such as splicing, copy-move, and removal.

Towards Trajectory Forecasting From Detection.

Trajectory forecasting for traffic participants (e.g., vehicles) is critical for autonomous platforms to make safe plans.

Social-Aware Pedestrian Trajectory Prediction via States Refinement LSTM.

In the task of pedestrian trajectory prediction, social interaction could be one of the most complicated factors since it is difficult to be interpreted through simple rules. Recent studies have shown a great ability of LSTM networks in learning social behaviors from datasets, e.g.

Progressive Temporal-Spatial-Semantic Analysis of Driving Anomaly Detection and Recounting.

For analyzing the traffic anomaly within dashcam videos from the perspective of ego-vehicles, the agent should spatial-temporally localize the abnormal occasion and regions and give a semantically recounting of what happened. Most existing formulations concentrate on the former spatial-temporal aspect and mainly approach this goal by training normal pattern classifiers/regressors/dictionaries with large-scale availably labeled data. However, anomalies are context-related, and it is difficult to distinguish the margin of abnormal and normal clearly.

EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks.

Recurrent neural networks (RNNs) are capable of modeling temporal dependencies of complex sequential data. In general, current available structures of RNNs tend to concentrate on controlling the contributions of current and previous information. However, the exploration of different importance levels of different elements within an input vector is always ignored.

View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition.

Skeleton-based human action recognition has recently attracted increasing attention thanks to the accessibility and the popularity of 3D skeleton data. One of the key challenges in action recognition lies in the large variations of action representations when they are captured from different viewpoints. In order to alleviate the effects of view variations, this paper introduces a novel view adaptation scheme, which automatically determines the virtual observation viewpoints over the course of an action in a learning based data driven manner.

Unifying Sum and Weighted Aggregations for Efficient yet Effective Image Representation Computation.

Embedding and aggregating a set of local descriptors (e.g. SIFT) into a single vector is normally used to represent images in image search.

A High Spatial Resolution Depth Sensing Method Based on Binocular Structured Light.

Depth information has been used in many fields because of its low cost and easy availability, since the Microsoft Kinect was released. However, the Kinect and Kinect-like RGB-D sensors show limited performance in certain applications and place high demands on accuracy and robustness of depth information. In this paper, we propose a depth sensing system that contains a laser projector similar to that used in the Kinect, and two infrared cameras located on both sides of the laser projector, to obtain higher spatial resolution depth information.

Video Object Discovery and Co-Segmentation with Extremely Weak Supervision.

We present a spatio-temporal energy minimization formulation for simultaneous video object discovery and co-segmentation across multiple videos containing irrelevant frames. Our approach overcomes a limitation that most existing video co-segmentation methods possess, i.e.

A supervoxel-based segmentation method for prostate MR images.

Purpose: Segmentation of the prostate on MR images has many applications in prostate cancer management. In this work, we propose a supervoxel-based segmentation method for prostate MR images.

Methods: A supervoxel is a set of pixels that have similar intensities, locations, and textures in a 3D image volume.

Efficient lossy compression for compressive sensing acquisition of images in compressive sensing imaging systems.

Compressive Sensing Imaging (CSI) is a new framework for image acquisition, which enables the simultaneous acquisition and compression of a scene. Since the characteristics of Compressive Sensing (CS) acquisition are very different from traditional image acquisition, the general image compression solution may not work well. In this paper, we propose an efficient lossy compression solution for CS acquisition of images by considering the distinctive features of the CSI.

Joint Segmentation and Recognition of Categorized Objects from Noisy Web Image Collection.

The segmentation of categorized objects addresses the problem of joint segmentation of a single category of object across a collection of images, where categorized objects are referred to objects in the same category. Most existing methods of segmentation of categorized objects made the assumption that all images in the given image collection contain the target object. In other words, the given image collection is noise free.

Spatio-temporal saliency perception via hypercomplex frequency spectral contrast.

Salient object perception is the process of sensing the salient information from the spatio-temporal visual scenes, which is a rapid pre-attention mechanism for the target location in a visual smart sensor. In recent decades, many successful models of visual saliency perception have been proposed to simulate the pre-attention behavior. Since most of the methods usually need some ad hoc parameters or high-cost preprocessing, they are difficult to rapidly detect salient object or be implemented by computing parallelism in a smart sensor.

Saliency detection based on biological plausibility of hypercomplex Fourier spectrum contrast.

This Letter proposes a novel saliency detection method based on biological plausibility of a hypercomplex Fourier spectrum contrast algorithm. The proposed algorithm takes into consideration not only simulation of simple cortical cells in the receptive field of humans but also the texture-color feature global spectrum contrast of an image. First, we utilize log-Gabor filters to mimic simple cortical cells in the receptive field of humans.

PSF estimation via gradient domain correlation.

This paper proposes an efficient method to estimate the point spread function (PSF) of a blurred image using image gradients spatial correlation. A patch-based image degradation model is proposed for estimating the sample covariance matrix of the gradient domain natural image. Based on the fact that the gradients of clean natural images are approximately uncorrelated to each other, we estimated the autocorrelation function of the PSF from the covariance matrix of gradient domain blurred image using the proposed patch-based image degradation model.

Proto-object based rate control for JPEG2000: an approach to content-based scalability.

The JPEG2000 system provides scalability with respect to quality, resolution and color component in the transfer of images. However, scalability with respect to semantic content is still lacking. We propose a biologically plausible salient region based bit allocation mechanism within the JPEG2000 codec for the purpose of augmenting scalability with respect to semantic content.

Tracking multiple visual targets via particle-based belief propagation.

Multiple-target tracking in video (MTTV) presents a technical challenge in video surveillance applications. In this paper, we formulate the MTTV problem using dynamic Markov network (DMN) techniques. Our model consists of three coupled Markov random fields: 1) a field for the joint state of the multitarget; 2) a binary random process for the existence of each individual target; and 3) a binary random process for the occlusion of each dual adjacent target.