An ROIs based pedestrian detection system for single images

Recently, pedestrian detection system has become an important issue to improve the road safety through its integration in several Advanced Driver Assistance Systems (ADAS). According to the literature, the use of the Histogram of Oriented Gradients (HOG) descriptor has been very efficient and robust in the development of such systems. However, the intensive calculation steps of this descriptor make its implementation an issue to be address in order to meet real-time requirement. In this paper, an efficient hardware architecture based on an improved HOG version and linear SVM (Support Vector Machine) classifier is emphasized. The proposed architecture has founded on cell-based scanning, some approximations and without any pixelbuffers. The system evaluation was carried out on a Xilinx Zynq platform. Experimental results showed that our approach can achieve an efficient pedestrian detection for full HD (High-Definition) video with a reduced hardware resource and power consumption.

Image segmentation plays an important role in machine vision, image recognition, and imaging applications. Based on the fuzzy c-means clustering algorithm, a kernel-distance-based intuitionistic fuzzy c-means clustering (KIFCM) algorithm is proposed. First, a fuzzy complement operator is used to generate the membership degree whereby the hesitation degree of intuitionistic fuzzy set is generated; second, a kernel-induced function is used to calculate the distance from each point to the cluster center instead of the Euclidean distance; third, a new objective function that includes the hesitation degree is established, and the optimization of the objective function results in new iterative expressions for the membership degree and the cluster center. The proposed KIFCM algorithm is compared with the fuzzy c-means clustering (FCM) algorithm, the kernel fuzzy c-means clustering (KFCM) algorithm, and the intuitionistic fuzzy c-means clustering (IFCM) algorithm in segmenting five images. The experimental results verify the effectiveness and superiority of our proposed KIFCM algorithm.

This paper describes a close to real-time scale invariant implementation of a pedestrian detector system which is based on the Histogram of Oriented Gradients (HOG) principle. Salient HOG features are first selected from a manually created very large database of samples with an evolutionary optimization procedure that directly trains a polynomial Support Vector Machine (SVM). Real-time operation is achieved by a cascaded 2-step classifier which uses first a very fast linear SVM (with the same features as the polynomial SVM) to reject most of the irrelevant detections and then computes the decision function with a polynomial SVM on the remaining set of candidate detections. Scale invariance is achieved by running the detector of constant size on scaled versions of the original input images and by clustering the results over all resolutions. The pedestrian detection system has been implemented in two versions: i) fully body detection, and ii) upper body only detection. The latter is especially suited for very busy and crowded scenarios. On a state-of-the-art PC it is able to run at a frequency of 8 - 20 frames/sec.

Feature selection using competitive coevolution of bio-inspired algorithms for the diagnosis of pulmonary emphysema

In this study, the concepts of competitive agglomeration clustering algorithm is incorporated into fuzzy c-means (FCM) clustering algorithm for symbolic interval-values data. In the proposed approach, called as IFCMwUNC clustering algorithm, the problems of the unknown clusters number and the initialization of prototypes in the FCM clustering algorithm for symbolic interval-values data are overcome and discussed. Due to the competitive agglomeration clustering algorithm possess the advantages of the hierarchical clustering algorithm and the partitional clustering algorithm, IFCMwUNC clustering algorithm can be fast converges in a few iterations regardless of the initial number of clusters. Moreover, it is also converges to the same optimal partition regardless of its initialization. Experiments results show the merits and usefulness of IFCMwUNC clustering algorithm for the symbolic interval-values data.

An image segmentation method based on a modified local-information weighted intuitionistic Fuzzy C-means clustering and Gold-panning Algorithm

There is always much difficult in the MR image segmentation. Although fuzzy c-means(FCM) clustering algorithm has been widely used in the field of image segmentation study, some inherent deficiencies of this algorithm especially the high cost of computation made the algorithm to be difficult widely used in practice. A novel algorithm, based on kernel fuzzy c-means (KFCM) clustering algorithm and the k-nearest neighbor (KNN) algorithm, is proposed to improve the performance of MR image segmentation. In this algorithm, the statistical gray level histogram of image is used in KFCM algorithm to speed up the algorithm. Furthermore, the spatial information of image is also considered by k-nearest neighbor algorithm based on kernel methods. With kernel methods each pixel of image is mapped into a high-dimensional feature space where FCM algorithm and KNN algorithm are carried out. Experiments show that the proposed algorithm is effective and efficient in image segmentation.

Pedestrian detection is an important but challenging component of an Intelligent Transportation System. In this paper, we describe a pedestrian detection system based on a monocular vision with a Far-Infrared camera (FIR). We propose an original feature representation, called Intensity Self Similarity (ISS), adapted to pedestrian detection in FIR images. The ISS representation is based on the relative intensity self similarity within a pedestrian region of interest (ROI) hypothesis. Our system consists of two components. The first component generates pedestrian ROI hypothesis by exploiting the specific characteristics of FIR images, where pedestrian shapes may vary in large scale, but heads appear usually as light regions. Pedestrian ROI are detected, with high recall rate, due to a Hierarchical Codebook (HC) of Speeded-Up Robust Features (SURF) located in light head regions. The second component consists of pedestrian hypothesis validation, by using a pedestrian full-body classification based on the ISS representation, with Support Vector Machine (SVM). For classification, we retained two feature descriptors: the Histogram of Oriented Gradients (HOG) descriptor and the original ISS feature representation that we proposed for FIR images. The early fusion of these two features enhances significantly the system precision, attaining an F-measure for the pedestrian class of 97.7%. Moreover, this feature fusion outperforms the state-of-the-art SURF descriptor proposed previously. The experimental evaluation shows that our pedestrian detector is also robust, since it performs well in detecting pedestrians even in large scale and crowded real-world scenes.

Traditional library systems are gradually being replaced by digital libraries. Digital libraries are developing from simple database-based storage and retrieval to knowledge-based implementation. The fuzzy C-means (FCM) clustering algorithm is an example of data collection and data processing technology. It evaluates and draws conclusions based on mathematics, large data, and other technologies. In order to better improve the digital library management system, this paper applied FCM clustering algorithm to the digital library management system. Based on the in-depth study of the FCM clustering algorithm, this paper built a digital library management system. The clustering algorithm was used to cluster library borrowing records and reader information. It provided technical support and suggestions on library collection construction and book purchase and promoted book management to form a good spitting cycle. The experimental results extracted during the evaluation phase demonstrated that the overall error rate of the suggested FCM clustering algorithm for information clustering is 3.66%, which is better than the existing comparative models. This shows that applying the FCM clustering algorithm to a digital library management system has some practical significance.

Fuzzy C-means (FCM) clustering algorithm is an important and popular clustering algorithm which is utilized in various application domains such as pattern recognition, machine learning, and data mining. Although this algorithm has shown acceptable performance in diverse problems, the current literature does not have studies about how they can improve the clustering quality of partitions with overlapping classes. The better the clustering quality of a partition, the better is the interpretation of the data, which is essential to understand real problems. This work proposes two robust FCM algorithms to prevent ambiguous membership into clusters. For this, we compute two types of weights: an weight to avoid the problem of overlapping clusters; and other weight to enable the algorithm to identify clusters of different shapes. We perform a study with synthetic datasets, where each one contains classes of different shapes and different degrees of overlapping. Moreover, the study considered real application datasets. Our results indicate such weights are effective to reduce the ambiguity of membership assignments thus generating a better data interpretation.

Many components of intelligent transportation systems require different levels of the traffic flow forecasting. This paper presents a novel short-term traffic flow forecasting model using a distributed radial basis function neural network (RBFNN) based on adaptive fuzzy c-means (FCM) clustering algorithm. FCM clustering algorithm is used to classify training objects into a couple of clusters, each cluster is trained by a sub RBFNN, and membership values are used for combining several RBFNN outputs to obtain the final result. In the online stage, the membership values are computed using an adaptive fuzzy clustering algorithm for the new object. The real traffic data are used to demonstrate the effectiveness of the method.

Novel outline features for pedestrian detection system with thermal images

Mammography is among the most popular imaging techniques used in the diagnosis of breast cancer. Nevertheless distinguishing between healthy and ill images is hard even for an experienced radiologist, because a single image usually includes several regions of interest (ROIs). The hardness of this classification problem along with the substantial amount of data, gathered from patients' medical history, motivates the use of a machine learning approach as part of a CAD (Computer Aided Detection) tool, aiming to assist radiologists in the characterization of mammography images. Specifically, our approach involves: i) the ROI extraction, ii) the Feature Vector extraction, iii) the Support Vector Machine (SVM) classification of ROIs and iv) the characterization of the whole image. We evaluate the performance of our approach in terms of the SVM's training and testing error and in terms of ROI specificity—sensitivity. The results show a relation between the number of features used and the SVM's performance.

Gold immunochromatographic strip (GICS) quantitative detective can provide more information than the qualitative or semiquantitative testing. In this paper, a fast color image segmentation method is presented to develop the quantitative detective of GICS. The image of GICS was acquired by Charge-coupled Device (CCD) image sensor, and segmented by fuzzy c-means (FCM) clustering algorithm based on color histogram in HSV color space. Maximin-distance algorithm was adopted to get the initial positions of centroids and cluster number to overcome the shortcoming that the FCM algorithm may produce local optimal results. For the segmented target image, a special characteristic parameter was constructed and calculated in HSV color space to achieve the quantitative interpretation of the GICS.

In this paper, a computational framework is proposed to perform a fully automatic segmentation of the left ventricle (LV) cavity from short-axis cardiac magnetic resonance (CMR) images. In the initial phase, the region of interest (ROI) is automatically identified on the first image frame of the CMR slices. This is done by partitioning the image into different regions using a standard fuzzy c-means (FCM) clustering algorithm where the LV region is identified according to its intensity, size and circularity in the image. Next, LV segmentation is performed within the identified ROI by using a novel clustering method that utilizes an objective functional with a dissimilarity measure that incorporates a circular shape function. This circular shape-constrained FCM algorithm is able to differentiate pixels with similar intensity but are located in different regions (e.g. LV cavity and non-LV cavity), thus improving the accuracy of the segmentation even in the presence of papillary muscles. In the final step, the segmented LV cavity is propagated to the adjacent image frame to act as the ROI. The segmentation and ROI propagation are then iteratively executed until the segmentation has been performed for the whole cardiac sequence. Experiment results using the LV Segmentation Challenge validation datasets show that our proposed framework can achieve an average perpendicular distance (APD) shift of 2.23±0.50mm and the Dice metric (DM) index of 0.89±0.03, which is comparable to the existing cutting edge methods. The added advantage over state of the art is that our approach is fully automatic, does not need manual initialization and does not require a prior trained model.