嵌入式智能图像处理在火灾预警系统的应用

传统的火灾检测方法一般采用感烟、感温、感光探测器等进行探测。本文提出了一种嵌入式基于图像视觉特征的火灾检测方法,以TI公司的数字多媒体处理器TMS320DM642为核心,设计实现智能前端火灾探测与自动报警系统。通过DM642对视频图像进行采集并结合相应的智能图像处理与模式识别算法,对森林火险进行实时监控。实验结果表明,该系统比传统系统更进一步减少了误报率且具有响应快、监控范围广等优点。

心理旋转中的整体优先效应及其机制

In the history of psychology research, more attention had been focused on the relation between local processing and global processing. For the global information and the local information, which is processed earlier? And which is processed faster? Precedence of the global over the local level in visual perception has been well established by Navon with compound stimuli, and Navon’s original study gave rise to many publications, including replications, generalization to other kinds of stimuli (nonverbal material, digits), populations (infants, children, brain-damaged subjects), and tasks (lateral visual hemifield presentation, copy drawing, memory recognition, and recall), and triggered some debate about the conditions in which global precedence is and is not observed (number, size, sparsity, and goodness of the stimuli, exposure duration, etc.). However, whether there is a global advantage or precedence in other cognitive processes was less tested. Most researches had suggested that there was a functional equivalency between visual perception and visual image processing. However, it’s still unknown whether there will be a global advantage on mental rotation. In the present study, we combined the mental rotation task with the compound stimuli to explore whether the global or local advantage also existed at the mental imagery transformation stages. In two pilot studies, the perceptual global precedence was found to be present in a normal/mirror-image judgment task when the stimuli exposure time was short; while the stimuli exposure time was prolonged (stimuli kept available till subjects’ response) the perceptual global precedence was showed to be eliminated. In all of the subsequent experiments, stimili would be presented till subjects’ response. Then mental rotation was added in normal/mirror-image judgment (some of the stimuli were rotated to certain angles from upright) in normal experiments, experiment 1 and 2 observed a global advantage on mental rotation both with a focused-attention design (Experiment 1) and divided-attention design (Experiment 2). Subjects’ reaction times were increased with rotation angles, and the accuracy was decreased with rotation angles, suggesting that subject need a mental rotation to make a normal/mirror judgment. The most important results were that subjects’ response to global rotation was faster than that to local rotation. The analysis of slope of rotation further indicated that, to some extend, the speed of global rotation was faster than that of local rotation. These results suggest a global advantage on mental rotation. Experiment 3 took advantage of the high temporal resolution of event-related potentials to explore the temporal pattern of global advantage on mental rotation. Event-related potential results indicated the parietal P300 amplitude was inversely related to the character orientation, and the local rotation task delayed the onset of the mental-rotation-related negativity at parietal electrodes. None clear effect was found for occipital N150. All these results suggested that the global rotation was not only processed faster than local rotation, but also occurred earlier than local rotation. Experiments 4 and 5 took the effect size of global advantage as the main dependent variable, and visual angle and exposure duration of the stimuli as independent variables, to examine the relationship between perceptual global precedence and global advantage on mental rotation. Results indicated that visual angle and exposure duration did not influence the effect size of global advantage on mental rotation. The global advantage on mental rotation and the perceptual global advantage seemed to be independent but their effects could be accumulated at some condition. These findings not only contribute to revealing a new processing property of mental rotation, but also deepen our understanding of the problem of global/local processing and shed light on the debate on locus of global precedence.

Visual pattern recognition network: Its training algorithm and its optoelectronic architecture

A visual pattern recognition network and its training algorithm are proposed. The network constructed of a one-layer morphology network and a two-layer modified Hamming net. This visual network can implement invariant pattern recognition with respect to image translation and size projection. After supervised learning takes place, the visual network extracts image features and classifies patterns much the same as living beings do. Moreover we set up its optoelectronic architecture for real-time pattern recognition. (C) 1996 Optical Society of America

An integrated aurora image retrieval system: AuroraEye

With the digital all-sky imager (ASI) emergence in aurora research, millions of images are captured annually. However, only a fraction of which can be actually used. To address the problem incurred by low efficient manual processing, an integrated image analysis and retrieval system is developed. For precisely representing aurora image, macroscopic and microscopic features are combined to describe aurora texture. To reduce the feature dimensionality of the huge dataset, a modified local binary pattern (LBP) called ALBP is proposed to depict the microscopic texture, and scale-invariant Gabor and orientation-invariant Gabor are employed to extract the macroscopic texture. A physical property of aurora is inducted as region features to bridge the gap between the low-level visual features and high-level semantic description. The experiments results demonstrate that the ALBP method achieves high classification rate and low computational complexity. The retrieval simulation results show that the developed retrieval system is efficient for huge dataset. (c) 2010 Elsevier Inc. All rights reserved.

Color to Gray: Visual Cue Preservation

Both commercial and scientific applications often need to transform color images into gray-scale images, e. g., to reduce the publication cost in printing color images or to help color blind people see visual cues of color images. However, conventional color to gray algorithms are not ready for practical applications because they encounter the following problems: 1) Visual cues are not well defined so it is unclear how to preserve important cues in the transformed gray-scale images; 2) some algorithms have extremely high time cost for computation; and 3) some require human-computer interactions to have a reasonable transformation. To solve or at least reduce these problems, we propose a new algorithm based on a probabilistic graphical model with the assumption that the image is defined over a Markov random field. Thus, color to gray procedure can be regarded as a labeling process to preserve the newly well-defined visual cues of a color image in the transformed gray-scale image. Visual cues are measurements that can be extracted from a color image by a perceiver. They indicate the state of some properties of the image that the perceiver is interested in perceiving. Different people may perceive different cues from the same color image and three cues are defined in this paper, namely, color spatial consistency, image structure information, and color channel perception priority. We cast color to gray as a visual cue preservation procedure based on a probabilistic graphical model and optimize the model based on an integral minimization problem. We apply the new algorithm to both natural color images and artificial pictures, and demonstrate that the proposed approach outperforms representative conventional algorithms in terms of effectiveness and efficiency. In addition, it requires no human-computer interactions.

Visual-Context Boosting for Eye Detection

Eye detection plays an important role in many practical applications. This paper presents a novel two-step scheme for eye detection. The first step models an eye by a newly defined visual-context pattern (VCP), and the second step applies semisupervised boosting for precise detection. VCP describes both the space and appearance relations between an eye region (region of eye) and a reference region (region of reference). The context feature of a VCP is extracted by using the integral image. Aiming to reduce the human labeling efforts, we apply semisupervised boosting, which integrates the context feature and the Haar-like features for precise eye detection. Experimental results on several standard face data sets demonstrate that the proposed approach is effective, robust, and efficient. We finally show that this approach is ready for practical applications.

Local Feature Based Geometric-Resistant Image Information Hiding

Watermarking aims to hide particular information into some carrier but does not change the visual cognition of the carrier itself. Local features are good candidates to address the watermark synchronization error caused by geometric distortions and have attracted great attention for content-based image watermarking. This paper presents a novel feature point-based image watermarking scheme against geometric distortions. Scale invariant feature transform (SIFT) is first adopted to extract feature points and to generate a disk for each feature point that is invariant to translation and scaling. For each disk, orientation alignment is then performed to achieve rotation invariance. Finally, watermark is embedded in middle-frequency discrete Fourier transform (DFT) coefficients of each disk to improve the robustness against common image processing operations. Extensive experimental results and comparisons with some representative image watermarking methods confirm the excellent performance of the proposed method in robustness against various geometric distortions as well as common image processing operations.