Shape-based recognition of wiry objects

We present an approach to the recognition of complex-shaped objects in cluttered environments based on edge information. We first use example images of a target object in typical environments to train a classifier cascade that determines whether edge pixels in an image belong to an instance of the desired object or the clutter. Presented with a novel image, we use the cascade to discard clutter edge pixels and group the object edge pixels into overall detections of the object. The features used for the edge pixel classification are localized, sparse edge density operations. Experiments validate the effectiveness of the technique for recognition of a set of complex objects in a variety of cluttered indoor scenes under arbitrary out-of-image-plane rotation. Furthermore, our experiments suggest that the technique is robust to variations between training and testing environments and is efficient at runtime.     

Text-based approaches for non-topical image categorization

The rapid expansion of multimedia digital collections brings to the fore the need for classifying not only text documents but their embedded non-textual parts as well. We propose a model for basing classification of multimedia on broad, non-topical features, and show how information on targeted nearby pieces of text can be used to effectively classify photographs on a first such feature, distinguishing between indoor and outdoor images. We examine several variations to a TF*IDF-based approach for this task, empirically analyze their effects, and evaluate our system on a large collection of images from current news newsgroups. In addition, we investigate alternative classification and evaluation methods, and the effects that secondary features have on indoor/outdoor classification. Using density estimation over the raw TF*IDF values, we obtain a classification accuracy of 82%, a number that outperforms baseline estimates and earlier, image-based approaches, at least in the domain of news articles, and that nears the accuracy of humans who perform the same task with access to comparable information. Published online: 22 September 2000     

The separation of reflected and transparent layers from real-world image sequence

This paper presents an optimal method for the separation of reflected and transparent layers from real-world scene images. Whereas past research has been applied to indoor environments and static cameras, our technique can be used for outdoor scenes and motion cameras. The method is based on spatio-temporal analysis, especially using epipolar plane images (EPI). The edge and color information of EPI has been used to segment the areas on EPIs efficiently and separate the reflected and transparent layers. This method can be used for refining building textures by removing reflections from captured images for the purpose of city modeling.     

Edge Detection in Range Images Based on Scan Line Approximation

In this paper we present a novel edge detection algorithm for range images based on a scan line approximation technique. Compared to the known methods in the literature, our algorithm has a number of advantages. It provides edge strength measures that have a straightforward geometric interpretation and supports a classification of edge points into several subtypes. We give a definition of optimal edge detectors and compare our algorithm to this theoretical model. We have carried out extensive tests using real range images acquired by four range scanners with quite different characteristics. Using a simple contour closure technique, we show that our edge detection method is able to achieve a complete range image segmentation into regions. This edge-based segmentation approach turns out to be superior to many region-based methods with regard to both segmentation quality and computational efficiency. The good results that were achieved demonstrate the practical usefulness of our edge detection algorithm.     

基于LBP和小波纹理特征的室内室外场景分类算法

室内室外场景识别是图像处理的基本问题之一。对此问题提出了一种不需要图像分割和目标识别的方法。首先,利用小波变换对原图像进行五层小波分解,然后用旋转不变的LBP(local binary pattern)算法对第二层至第五层的LL子图提取纹理特征,再计算第二层和第一层LH,HL,HH子图的能量均值和方差,最后连接这两个过程形成的低维特征向量进行室内室外图像场景分类。实验结果表明此算法分类效果比较好,且识别室内室外场景并不需要目标形状等局部详细信息,只需要图像模糊化的全局纹理信息。     

一种基于贝叶斯网络的视频静态语义探测新方法

传统的基于内容的视频检索是利用图像的颜色、纹理以及形状等底层特征来对视频进行检索,然而这些底层特征并没有深层次地挖掘出视频的语义内容。在用支持矢量机对图像进行分类的基础上,提出了一种基于贝叶斯网络的对视频静态语义如室内/室外进行探测的新方法,实验结果验证该方法的有效性。     

一种基于贝叶斯网络的视频静态语义探测新方法

传统的基于内容的视频检索是利用图像的颜色、纹理以及形状等底层特征来对视频进行检索,然而这些底层特征并没有深层次地挖掘出视频的语义内容。在用支持矢量机对图像进行分类的基础上,提出了一种基于贝叶斯网络的对视频静态语义如室内/室外进行探测的新方法,实验结果验证该方法的有效性。     

Improved scene classification using efficient low-level features and semantic cues

Prior research in scene classification has focused on mapping a set of classic low-level vision features to semantically meaningful categories using a classifier engine. In this paper, we propose improving the established paradigm by using a simplified low-level feature set to predict multiple semantic scene attributes that are integrated probabilistically to obtain a final indoor/outdoor scene classification. An initial indoor/outdoor prediction is obtained by classifying computationally efficient, low-dimensional color and wavelet texture features using support vector machines. Similar low-level features can also be used to explicitly predict the presence of semantic features including grass and sky. The semantic scene attributes are then integrated using a Bayesian network designed for improved indoor/outdoor scene classification.     

Multi-modal time-of-flight based fire detection

This paper proposes two novel time-of-flight based fire detection methods for indoor and outdoor fire detection. The indoor detector is based on the depth and amplitude image of a time-of-flight camera. Using this multi-modal information, flames can be detected very accurately by fast changing depth and amplitude disorder detection. In order to detect the fast changing depth, depth differences between consecutive frames are accumulated over time. Regions which have multiple pixels with a high accumulated depth difference are labeled as candidate flame regions. Simultaneously, the amplitude disorder is also investigated. Regions with high accumulative amplitude differences and high values in all detail images of the amplitude image its discrete wavelet transform, are also labeled as candidate flame regions. Finally, if one of the depth and amplitude candidate flame regions overlap, fire alarm is given. The outdoor detector, on the other hand, only differs from the indoor detector in one of its multi-modal inputs. As depth maps are unreliable in outdoor environments, the outdoor detector uses a visual flame detector instead of the fast changing depth detection. Experiments show that the proposed detectors have an average flame detection rate of 94% with no false positive detections.     

Filter-based models for pattern classification

In this paper we consider a technique for pattern classification based upon the development of prototypes which capture the distinguishing features (“disjunctive prototypes”) of each pattern class and, via cross-correlation with incoming test images, enable efficient pattern classification. We evaluate such a classification procedure with prototypes based on the images per se (direct code), Gabor scheme (multiple fixed filter representation) and an edge (scale space-based) coding scheme. Our analyses, and comparisons with human pattern classification performance, indicate that the edge-only disjunctive prototypes provide the most discriminating classification performance and are the more representative of human behaviour.     

Learning geometric and photometric features from panoramic LiDAR scans for outdoor place categorization

Semantic place categorization, which is one of the essential tasks for autonomous robots and vehicles, allows them to have capabilities of self-decision and navigation in unfamiliar environments. In particular, outdoor places are more difficult targets than indoor ones due to perceptual variations, such as dynamic illuminance over 24 hours and occlusions by cars and pedestrians. This paper presents a novel method of categorizing outdoor places using convolutional neural networks (CNNs), which take omnidirectional depth/reflectance images obtained by 3D LiDARs as the inputs. First, we construct a large-scale outdoor place dataset named Multi-modal Panoramic 3D Outdoor (MPO) comprising two types of point clouds captured by two different LiDARs. They are labeled with six outdoor place categories: coast, forest, indoor/outdoor parking, residential area, and urban area. Second, we provide CNNs for LiDAR-based outdoor place categorization and evaluate our approach with the MPO dataset. Our results on the MPO dataset outperform traditional approaches and show the effectiveness in which we use both depth and reflectance modalities. To analyze our trained deep networks, we visualize the learned features.     

ON IMAGE CLASSIFICATION: CITY IMAGES VS. LANDSCAPES

Grouping images into semantically meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Based on these groupings, effective indices can be built for an image database. In this paper, we show how a specific high-level classification problem (city images vs landscapes) can be solved from relatively simple low-level features geared for the particular classes. We have developed a procedure to qualitatively measure the saliency of a feature towards a classification problem based on the plot of the intra-class and inter-class distance distributions. We use this approach to determine the discriminative power of the following features: color histogram, color coherence vector, DCT coefficient, edge direction histogram, and edge direction coherence vector. We determine that the edge direction-based features have the most discriminative power for the classification problem of interest here. A weighted k-NN classifier is used for the classification which results in an accuracy of 93.9% when evaluated on an image database of 2716 images using the leave-one-out method. This approach has been extended to further classify 528 landscape images into forests, mountains, and sunset/sunrise classes. First, the input images are classified as sunset/sunrise images vs forest & mountain images (94.5% accuracy) and then the forest & mountain images are classified as forest images or mountain images (91.7% accuracy). We are currently identifying further semantic classes to assign to images as well as extracting low level features which are salient for these classes. Our final goal is to combine multiple 2-class classifiers into a single hierarchical classifier.     

An edge detection framework conjoining with IMU data for assisting indoor navigation of visually impaired persons

Smartphone applications based on object detection techniques have recently been proposed to assist visually impaired persons with navigating indoor environments. In the smartphone, digital cameras are installed to detect objects which are important for navigation. Prior to detect the interested objects from images, edges on the objects have to be identified. Object edges are difficult to be detected accurately as the image is contaminated by strong image blur which is caused by camera movement. Although deblurring algorithms can be used to filter blur noise, they are computationally expensive and not suitable for real-time implementation. Also edge detection algorithms are mostly developed for stationary images without serious blur. In this paper, a modified sigmoid function (MSF) framework based on inertial measurement unit (IMU) is proposed to mitigate these problems. The IMU estimates blur levels to adapt the MSF which is computationally simple. When the camera is moving, the topological structure of the MSF is estimated continuously in order to improve effectiveness of edge detections. The performance of the MSF framework is evaluated by detecting object edges on video sequences associated with IMU data. The MSF framework is benchmarked against existing edge detection techniques and results show that it can obtain comparably lower errors. It is further shown that the computation time is significantly decreased compared to using techniques that deploy deblurring algorithms, thus making our proposed technique a strong candidate for reliable real-time navigation.     

Road model prediction based unstructured road detection

Vision-based road detection is an important research topic in different areas of computer vision such as the autonomous navigation of mobile robots. In outdoor unstructured environments such as villages and deserts, the roads are usually not well-paved and have variant colors or texture distributions. Traditional region- or edge-based approaches, however, arc effective only in specific environments, and most of them have weak adaptability to varying road types and appearances. In this paper we describe a novel top-down based hybrid algorithm which properly combines both region and edge cues from the images. The main difference between our proposed algorithm and previous ones is that, before road detection, an off-line scene classifier is efficiently learned by both low- and high-level image cues to predict the unstructured road model. This scene classification can bc considered a decision process which guides the selection of the optimal solution from region- or edge-based approaches to detect the road. Moreover, a temporal smoothing mechanism is incorporated, which further makes both model prediction and region classification more stable. Experimental results demonstrate that compared with traditional region- and edge-based algorithms, our algorithm is more robust in detecting the road areas with diverse road types and varying appearances in unstructured conditions.     

基于802．11a／b MAC的WLAN用户定位技术研究

本文研究了包括IEEE 802，11a／b结构特征在内的IEEE 802，11无线局域网(WLAN)的特性，提出了基于IEEE 802．11a／b MAC的WLAN用户室内室外定位技术方案。     

Multiclass object classification for real-time video surveillance systems

Object classification in video is an important factor for improving the reliability of various automatic applications in video surveillance systems, as well as a fundamental feature for advanced applications, such as scene understanding. Despite extensive research, existing methods exhibit relatively moderate classification accuracy when tested on a large variety of real-world scenarios, or do not obey the real-time constraints of video surveillance systems. Moreover, their performance is further degraded in multi-class classification problems. We explore multi-class object classification for real-time video surveillance systems and propose an approach for classifying objects in both low and high resolution images (human height varies from a few to tens of pixels) in varied real-world scenarios. Firstly, we present several features that jointly leverage the distinction between various classes. Secondly, we provide a feature-selection procedure based on entropy gain, which screens out superfluous features. Experiments, using various classification techniques, were performed on a large and varied database consisting of ∼29,000 object instances extracted from 140 different real-world indoor and outdoor, near-field and far-field scenes having various camera viewpoints, which capture a large variety of object appearances under real-world environmental conditions. The insight raised from the experiments is threefold: the efficiency of our feature set in discriminating between classes, the performance improvement when using the feature selection method, and the high classification accuracy obtained on our real-time system on both DSP (TMS320C6415-6E3, 600 MHz) and PC (Quad Core Intel® Xeon® E5310, 2 × 4 MB Cache, 1.60 GHz, 1066 MHz) platforms.     

基于支持向量机多分类器的室内外场景感知算法

针对普适室内外场景持续感知面临的低功耗、复杂动态环境、异构使用模式带来的挑战,提出了一种轻量级的基于支持向量机多分类器的高精度、低功耗室内外场景检测算法.该算法使用智能手机集成的各种传感器(可见光传感器、磁传感器、加速度传感器、陀螺仪传感器和气压传感器),在挖掘分析各种传感器在室内外场景的不同特征,以及人们在室内外场景的行为差异基础上,根据时间和气象条件设计多个支持向量机分类器,对复杂室内外场景进行识别.实验结果表明,基于支持向量机多分类器的室内外场景检测算法具有较好的普适性,可获得95%以上的室内外判定准确率,平均功耗小于5 mW.     

Improving Face Recognition via Narrowband Spectral Range Selection Using Jeffrey Divergence

In order to achieve improved recognition performance in comparison with conventional broadband images, this paper addresses a new method that automatically specifies the optimal spectral range for multispectral face images according to given illuminations. The novelty of our method lies in the introduction of a distribution separation measure and the selection of the optimal spectral range by ranking these separation values. The selected spectral ranges are consistent with the physics analysis of the multispectral imaging process. The fused images from these chosen spectral ranges are verified to outperform the conventional broadband images by 3%–20%, based on a variety of experiments with indoor and outdoor illuminations using two well-recognized face-recognition engines. Our discovery can be practically used for a new customized sensor design associated with given illuminations for improved face-recognition performance over the conventional broadband images.      

基于无线局域网的位置定位技术研究和发展

随着无线局域网技术的飞速发展和无线局域网络在全世界范围内的广泛部署,一种基于无线局域网络覆盖的位置定位技术成为室内环境下位置服务研究的热点。基于无线局域网络的位置服务具有设施简单、覆盖范围广、服务可集成度高等特点,并能实现较高的定位精度。本文介绍了基于WLAN的定位技术,并结合目前国内外几个典型的定位系统研究分析了WLAN定位技术的特点和研究发展过程。     

Metric learning for weather image classification

Image classification is a core task in many applications of computer vision. Recognition of weather conditions based on large-volume image datasets is a challenging problem. However, there has been little research on weather-related recognition using color images, particularly with large datasets. In this study, we proposed a metric learning framework to investigate a two-class weather classification problem. We improve the classification accuracy using metric learning approaches. Extracting features from images is a challenging task and practical requirements such as domain knowledge and human intervention. In this paper, we define several categories of weather feature cures based on observations of outdoor images captured under different weather conditions. Experimental results show that a classifier based on metric learning framework is effective in weather classification and outperforms the previous approach when using the same dataset.