采用人体树图与混合粒子群聚类的行人检测

为提升辅助驾驶系统的可靠性及安全系数,实现更高精度的行人检测,基于人体树图模型提出了一种改进的离线训练、在线检测的行人检测方法。首先,定义人体部件间的共生关系,得到对应父子部件对,结合K-means算法对其位置关系进行聚类获得部件类型。为兼顾类内紧密性与类间分离性,采用MSE和DBI构建具有两阶段适应度函数的混合粒子群聚类算法,在有效估计各部件最优聚类中心数量的同时,消除随机初始化对聚类准确率造成的影响。其次,将优化聚类得到的部件类型作为隐藏变量,通过求解隐结构SVM获取改进后的人体检测模型。最后,通过动态规划算法求解状态转移方程,在多个尺度上有效估计人体部件位置及检测包围盒,并结合非极大值抑制思想得到最终的行人检测结果。实验结果表明,所提方法在检测性能上明显优于5种行人检测方法,并且相比于原始Pose-original方法,在INRIA和ETH数据集上的丢失率分别下降了8.14%和5.05%。实验证明该方法检测性能良好且具有较高的准确性和鲁棒性。     

Adaptive striping watershed segmentation method for processing microscopic images of overlapping irregular‐shaped and multicentre particles

Oversegmentation is a major drawback of the morphological watershed algorithm. Here, we study and reveal that the oversegmentation is not only because of the irregular shapes of the particle images, which people are familiar with, but also because of some particles, such as ellipses, with more than one centre. A new parameter, the striping level, is introduced and the criterion for striping parameter is built to help find the right markers prior to segmentation. An adaptive striping watershed algorithm is established by applying a procedure, called the marker searching algorithm, to find the markers, which can effectively suppress the oversegmentation. The effectiveness of the proposed method is validated by analysing some typical particle images including the images of gold nanorod ensembles.     

Structure similarity‐guided image binarization for automatic segmentation of epidermis surface microstructure images

Partitioning epidermis surface microstructure (ESM) images into skin ridge and skin furrow regions is an important preprocessing step before quantitative analyses on ESM images. Binarization segmentation is a potential technique for partitioning ESM images because of its computational simplicity and ease of implementation. However, even for some state‐of‐the‐art binarization methods, it remains a challenge to automatically segment ESM images, because the grey‐level histograms of ESM images have no obvious external features to guide automatic assessment of appropriate thresholds. Inspired by human visual perceptual functions of structural feature extraction and comparison, we propose a structure similarity‐guided image binarization method. The proposed method seeks for the binary image that best approximates the input ESM image in terms of structural features. The proposed method is validated by comparing it with two recently developed automatic binarization techniques as well as a manual binarization method on 20 synthetic noisy images and 30 ESM images. The experimental results show: (1) the proposed method possesses self‐adaption ability to cope with different images with same grey‐level histogram; (2) compared to two automatic binarization techniques, the proposed method significantly improves average accuracy in segmenting ESM images with an acceptable decrease in computational efficiency; (3) and the proposed method is applicable for segmenting practical EMS images. (Matlab code of the proposed method can be obtained by contacting with the corresponding author.)     

Computer‐aided diagnosis software for vulvovaginal candidiasis detection from Pap smear images

Vulvovaginal candidiasis (VVC) is a common gynecologic infection and it occurs when there is overgrowth of the yeast called Candida. VVC diagnosis is usually done by observing a Pap smear sample under a microscope and searching for the conidium and mycelium components of Candida. This manual method is time consuming, subjective and tedious. Any diagnosis tools that detect VVC, semi‐ or full‐automatically, can be very helpful to pathologists. This article presents a computer aided diagnosis (CAD) software to improve human diagnosis of VVC from Pap smear samples. The proposed software is designed based on phenotypic and morphology features of the Candida in Pap smear sample images. This software provide a user‐friendly interface which consists of a set of image processing tools and analytical results that helps to detect Candida and determine severity of illness. The software was evaluated on 200 Pap smear sample images and obtained specificity of 91.04% and sensitivity of 92.48% to detect VVC. As a result, the use of the proposed software reduces diagnostic time and can be employed as a second objective opinion for pathologists.