Video saliency detection incorporating temporal information in compressed domain

Saliency detection is widely used to pick out relevant parts of a scene as visual attention regions for various image/video applications. Since video is increasingly being captured, moved and stored in compressed form, there is a need for detecting video saliency directly in compressed domain. In this study, a compressed video saliency detection algorithm is proposed based on discrete cosine transformation (DCT) coefficients and motion information within a visual window. Firstly, DCT coefficients and motion information are extracted from H.264 video bitstream without full decoding. Due to a high quantization parameter setting in encoder, skip/intra is easily chosen as the best prediction mode, resulting in a large number of blocks with zero motion vector and no residual existing in video bitstream. To address these problems, the motion vectors of skip/intra coded blocks are calculated by interpolating its surroundings. In addition, a visual window is constructed to enhance the contrast of features and to avoid being affected by encoder. Secondly, after spatial and temporal saliency maps being generated by the normalized entropy, a motion importance factor is imposed to refine the temporal saliency map. Finally, a variance-like fusion method is proposed to dynamically combine these maps to yield the final video saliency map. Experimental results show that the proposed approach significantly outperforms other state-of-the-art video saliency detection models.     

基于PC104总线的性能检测系统

为了适应外场装备检测装置的需求,以某型自行火炮的随动系统为例,基于PC104嵌入式控制总线的高效数据传榆特点,采用SCM/LX-3160主板和ADT620、CDT2000数据采集板,完成数据采集与转换的硬件配置.并把处理后数据通过Visual C＋＋软件开发工具进行实时显示,确定随动系统的工作状态.应用结果表明,该性能检测系统携带方便,能实时采集信号,并对信号进行准确的分析处理,具有较高的实时性和稳定性.     

基于Visua1C#2010的变频器上位机的设计开发

为了提高变频器自动化测试水平,提升生产效率,提出了在VisuaIC#2010开发环境下变频器上位机的设计方案。本文着重介绍了在上位机中如何利用NI Measurement Studio波形控件进行的虚拟示波器设计的方法,对通讯传输的动态数据如何实现波形化显示进行了详细分析说明。     

电子束纵向磁聚焦的计算机模拟

给出了用VB模拟电子束纵向磁聚焦的绘图子程序。     

弹丸参数测试系统的数据处理

弹丸参数测试系统主要用于弹速度的测量,它可以测量弹丸速度的大小。弹丸速度的大小是弹道特性的重要参数之一。弹丸的速度测量是枪炮、弹药研制生产和开发验收中最基本的试验项目。天幕靶可以更快、更准确地测量弹速。基于天幕靶并利用Visual C++6.0开发了一款弹参数测试系统软件,可实现弹参数测试系统的数据处理。     

A novel area measurement scheme based on a multi-resolution dynamic contour

A novel scheme for an object's surface area measurement is proposed, which is suitable for area computation of an object with smooth and irregular edges. A multi-resolution dynamic contour is applied and this scheme consists of four steps. Firstly, a photoelectric image collimation system is applied to obtain a target image of a detected object. An image pyramid for the target image is constructed by wavelet decomposition and reconstruction. Secondly, in the image pyramid, a multi-resolution dynamic contour converges to the target's contour edge from coarse to fine scale, via feature search and an iteration algorithm. Thirdly, for the convergent dynamic contour, two formulas for the area and centroid computation of a closed B-spline curve are applied to compute the image target's area and centroid exactly. Finally, a novel centroid self-calibration technology is applied, which measures the pixel's size equivalence with the computed centroid and a dual-frequency laser to measure the true object's surface area exactly. Experiments indicate that this scheme's single-measurement error decreases to ±0.2%, when the number of control points is 20. Compared to conventional measurement approaches, this scheme is robust for a target image with noises or complicated edges.     

Retinotopic mapping with spin echo BOLD at 7T

For blood oxygenation level-dependent (BOLD) functional MRI experiments, contrast-to-noise ratio (CNR) increases with increasing field strength for both gradient echo (GE) and spin echo (SE) BOLD techniques. However, susceptibility artifacts and nonuniform coil sensitivity profiles complicate large field-of-view fMRI experiments (e.g., experiments covering multiple visual areas instead of focusing on a single cortical region). Here, we use SE BOLD to acquire retinotopic mapping data in early visual areas, testing the feasibility of SE BOLD experiments spanning multiple cortical areas at 7T. We also use a recently developed method for normalizing signal intensity in T₁-weighted anatomical images to enable automated segmentation of the cortical gray matter for scans acquired at 7T with either surface or volume coils. We find that the CNR of the 7T GE data (average single-voxel, single-scan stimulus coherence: 0.41) is almost twice that of the 3T GE BOLD data (average coherence: 0.25), with the CNR of the SE BOLD data (average coherence: 0.23) comparable to that of the 3T GE data. Repeated measurements in individual subjects find that maps acquired with 1.8-mm resolution at 3T and 7T with GE BOLD and at 7T with SE BOLD show no systematic differences in either the area or the boundary locations for V1, V2 and V3, demonstrating the feasibility of high-resolution SE BOLD experiments with good sensitivity throughout multiple visual areas.     

Relationship between neural and hemodynamic signals during spontaneous activity studied with temporal kernel CCA

Functional magnetic resonance imaging (fMRI) based on the so-called blood oxygen level-dependent (BOLD) contrast is a powerful tool for studying brain function not only locally but also on the large scale. Most studies assume a simple relationship between neural and BOLD activity, in spite of the fact that it is important to elucidate how the “when” and “what” components of neural activity are correlated to the “where” of fMRI data. Here we conducted simultaneous recordings of neural and BOLD signal fluctuations in primary visual (V1) cortex of anesthetized monkeys. We explored the neurovascular relationship during periods of spontaneous activity by using temporal kernel canonical correlation analysis (tkCCA). tkCCA is a multivariate method that can take into account any features in the signals that univariate analysis cannot. The method detects filters in voxel space (for fMRI data) and in frequency–time space (for neural data) that maximize the neurovascular correlation without any assumption of a hemodynamic response function (HRF). Our results showed a positive neurovascular coupling with a lag of 4–5 s and a larger contribution from local field potentials (LFPs) in the γ range than from low-frequency LFPs or spiking activity. The method also detected a higher correlation around the recording site in the concurrent spatial map, even though the pattern covered most of the occipital part of V1. These results are consistent with those of previous studies and represent the first multivariate analysis of intracranial electrophysiology and high-resolution fMRI.     

A secure fingerprint template generation mechanism using visual secret sharing with inverse halftoning

Fingerprints are the most popular and widely practiced biometric trait for human recognition and authentication. Due to the wide approval, reliable fingerprint template generation and secure saving of the generated templates are highly vital. Since fingers are permanently connected to the human body, loss of fingerprint data is irreversible. Cancelable fingerprint templates are used to overcome this problem. This paper introduces a novel cancelable fingerprint template generation mechanism using Visual Secret Sharing (VSS), data embedding, inverse halftoning, and super-resolution. During the fingerprint template generation, VSS shares with some hidden information are formulated as the secure cancelable template. Before authentication, the secret fingerprint image is reconstructed back from the VSS shares. The experimental results show that the proposed cancelable templates are simple, secure, and fulfill all the properties of the ideal cancelable templates, such as security, accuracy, non-invertibility, diversity, and revocability. The experimental analysis shows that the reconstructed fingerprint images are similar to the original fingerprints in terms of visual parameters and matching error rates.     

VICTOR: Visual incompatibility detection with transformers and fashion-specific contrastive pre-training

For fashion outfits to be considered aesthetically pleasing, the garments that constitute them need to be compatible in terms of visual aspects, such as style, category and color. Previous works have defined visual compatibility as a binary classification task with items in a garment being considered as fully compatible or fully incompatible. However, this is not applicable to Outfit Maker applications where users create their own outfits and need to know which specific items may be incompatible with the rest of the outfit. To address this, we propose the Visual InCompatibility TransfORmer (VICTOR) that is optimized for two tasks: 1) overall compatibility as regression and 2) the detection of mismatching items and utilize fashion-specific contrastive language-image pre-training for fine tuning computer vision neural networks on fashion imagery. We build upon the Polyvore outfit benchmark to generate partially mismatching outfits, creating a new dataset termed Polyvore-MISFITs, that is used to train VICTOR. A series of ablation and comparative analyses show that the proposed architecture can compete and even surpass the current state-of-the-art on Polyvore datasets while reducing the instance-wise floating operations by 88%, striking a balance between high performance and efficiency. We release our code at https://github.com/stevejpapad/Visual-InCompatibility-Transformer