[show abstract][hide abstract] ABSTRACT: We propose a visibility estimation method for traffic signs considering temporal environmental changes, as a part of work for the realization of nuisance-free driver assistance systems. Recently, the number of driver assistance systems in a vehicle is increasing. Accordingly, it is becoming important to sort out appropriate information provided from them, because providing too much information may cause driver distraction. To solve such a problem, we focus on a visibility estimation method for controlling the information according to the visibility of a traffic sign. The proposed method sequentially captures a traffic sign by an in-vehicle camera, and estimates its accumulative visibility by integrating a series of instantaneous visibility. By this way, even if the environmental conditions may change temporally and complicatedly, we can still accurately estimate the visibility that the driver perceives in an actual traffic scene. We also investigate the performance of the proposed method and show its effectiveness.
[show abstract][hide abstract] ABSTRACT: We propose a visibility estimation method for traffic signs as part of work for realization of nuisance-free driving safety support systems. Recently, the number of driving safety support systems in a car has been increasing. As a result, it is becoming important to select appropriate information from them for safe and comfortable driving because too much information may cause driver distraction and may increase the risk of a traffic accident. One of the approaches to avoid such a problem is to alert the driver only with information which could easily be missed. Therefore, to realize such a system, we focus on estimating the visibility of traffic signs. The proposed method is a model-based method that estimates the visibility of traffic signs focusing on the difference of image features between a traffic sign and its surrounding region. In this paper, we investigate the performance of the proposed method and show its effectiveness.
[show abstract][hide abstract] ABSTRACT: We propose a method for construction of a cascaded traffic sign detector. Viola et al. have proposed a robust and extremely rapid object detection method based on a boosted cascade of simple feature classifiers. To obtain a high detection accuracy in real environment, it is necessary to train the classifier with a set of learning images which contain various appearances of detection targets. However, collecting the traffic sign images manually for training takes much cost. Therefore, we use a generative learning method for constructing the traffic sign detector. In this paper, shape, texture and color changes are considered in the generative learning. By this method, the performance of the traffic sign detection improves and the cost of collecting the training images is reduced at the same time. Experimental results using car-mounted camera images showed the effectiveness of the proposed method.
Innovative Computing, Information and Control (ICICIC), 2009 Fourth International Conference on; 01/2010
[show abstract][hide abstract] ABSTRACT: Wireless capsule endoscopy (WCE) is a new clinical technology permitting visualization of the small bowel, the most difficult segment of the digestive tract. The major drawback of this technology is the excessive amount of time required for video diagnosis. We therefore propose a method for generating smaller videos by detecting informative frames from original WCE videos. This method isolates useless frames that are highly contaminated by turbid fluids, faecal materials and/or residual foods. These materials and fluids are presented in a wide range of colors, from brown to yellow, and/or have bubble-like texture patterns. The detection scheme therefore consists of two steps: isolating (Step-1) highly contaminated non-bubbled (HCN) frames and (Step-2) significantly bubbled (SB) frames. Two color representations, viz., local color moments in Ohta space and the HSV color histogram, are attempted to characterize HCN frames, which are isolated by a support vector machine (SVM) classifier in Step-1. The rest of the frames go to Step-2, where a Gauss Laguerre transform (GLT) based multiresolution texture feature is used to characterize the bubble structures in WCE frames. GLT uses Laguerre Gauss circular harmonic functions (LG-CHFs) to decompose WCE images into multiresolution components. An automatic method of segmentation was designed to extract bubbled regions from grayscale versions of the color images based on the local absolute energies of their CHF responses. The final informative frames were detected by using a threshold on the segmented regions. An automatic procedure for selecting features based on analyzing the consistency of the energy-contrast map is also proposed. Three experiments, two of which use 14,841 and 37,100 frames from three videos and the rest uses 66,582 frames from six videos, were conducted for justifying the proposed method. The two combinations of the proposed color and texture features showed excellent average detection accuracies (86.42% and 84.45%) with the final experiment, when compared with the same color features followed by conventional Gabor-based (78.18% and 76.29%) and discrete wavelet-based (65.43% and 63.83%) texture features. Although intra-video training-testing cases are typical choices for supervised classification in Step-1, combining a suitable number of training sets using a subset of the input videos was shown to be possible. This mixing not only reduced computation costs but also produced better detection accuracies by minimizing visual-selection errors, especially when processing large numbers of WCE videos.
Medical image analysis 01/2010; 14(3):449-70. · 3.09 Impact Factor
[show abstract][hide abstract] ABSTRACT: We propose a method to recognize the visibility of traffic signals from a driver's perspective. The more that driver assistance systems are equipped for practical use, the more information that is being provided for drivers. So each information provision system should select appropriate information based on the situation. Our goal is to realize a system that quantifies the visibility of traffic signals from images taken by in-vehicle cameras and appropriately provides information to drivers. In this paper, we propose a method to measure visibility by two criterions, detectability and discriminability. Each index is computed using image processing techniques. Experiments using actual images showed that the proposed indices correspond well to human perception.
IEEJ Transactions on Electronics Information and Systems 01/2010; 130(6):1034-1041.
[show abstract][hide abstract] ABSTRACT: An eigenspace interpolation method smoothly interpolates between two different eigenspaces using high dimensional rotation. However, up to now its effectiveness in object recognition and the validity of the interpolation algorithm have not been discussed sufficiently. We therefore propose an appearance-based object recognition method combining the eigenspace interpolation method and a subspace method. We conducted face recognition experiments using images captured from multiple camera positions with various illumination conditions. Experimental results demonstrate the effectiveness of the proposed method and the validity of the interpolation algorithm.
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on; 01/2009
[show abstract][hide abstract] ABSTRACT: Three dimensional medical image registration is a fundamental technique which applied various medical treatments such as image diagnosis, treatment planning, image guided surgery, etc. In radiation therapy for cancer treatment, quick alignment method around target lesion is required. To align CT images, similarity measurement like normalized cross correlation or mutual information should be calculated. The computational cost must reduce for achieving the online image registration. In this paper, we propose a novel quick rigid registration method which makes it possible to align the three dimensional images using the parametric eigenspace method. By projecting each CT slice image into the eigenspace as a set of low dimensional vectors, image similarity can be calculated very rapidly. The experiments using CT images of the same patient, it is found that the alignment accuracy is almost the same as the method using normalized cross correlation, and the computation time is less than one second.
IEEJ Transactions on Electronics Information and Systems 01/2009; 129(9):1699-1704.
[show abstract][hide abstract] ABSTRACT: Wireless capsule endoscopy (WCE) is a new clinical technology permitting the visualization of the small bowel, the most difficult segment of the digestive tract. The major drawback of this technology is the high amount of time for video diagnosis. In this study, we propose a method for informative frame detection by isolating useless frames that are substantially covered by turbid fluids or their contamination with other materials, e.g., faecal, semi-processed or unabsorbed foods etc. Such materials and fluids present a wide range of colors, from brown to yellow, and/or bubble-like texture patterns. The detection scheme, therefore, consists of two stages: highly contaminated non-bubbled (HCN) frame detection and significantly bubbled (SB) frame detection. Local color moments in the Ohta color space are used to characterize HCN frames, which are isolated by the Support Vector Machine (SVM) classifier in Stage-1. The rest of the frames go to the Stage-2, where Laguerre gauss Circular Harmonic Functions (LG-CHFs) extract the characteristics of the bubble-structures in a multi-resolution framework. An automatic segmentation method is designed to extract the bubbled regions based on local absolute energies of the CHF responses, derived from the grayscale version of the original color image. Final detection of the informative frames is obtained by using threshold operation on the extracted regions. An experiment with 20,558 frames from the three videos shows the excellent average detection accuracy (96.75%) by the proposed method, when compared with the Gabor based- (74.29%) and discrete wavelet based features (62.21%).
[show abstract][hide abstract] ABSTRACT: Despite emerging technology, wireless capsule endoscopy needs high amount of diagnosis-time due to the presence of many useless frames, created by turbid fluids, foods, and faecal materials. These materials and fluids present a wide range of colors and/or bubble-like texture patterns. We, therefore, propose a cascade method for informative frame detection, which uses local color histogram to isolate highly contaminated non-bubbled (HCN) frames, and Gauss Laguerre Transform (GLT) based multiresolution norm-1 energy feature to isolate significantly bubbled (SB) frames. Supervised support vector machine is used to classify HCN frames (Stage-1), while automatic bubble segmentation followed by threshold operation (Stage-2) is adopted to detect informative frames by isolating SB frames. An experiment with 20,558 frames from the three videos shows 97.48% average detection accuracy by the proposed method, when compared with methods adopting Gabor based-(75.52%) and discrete wavelet based features (63.15%) with the same color feature.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 02/2008; 11(Pt 2):603-10.
[show abstract][hide abstract] ABSTRACT: Camera-based character recognition has gained attention with the growing use of camera-equipped portable devices. One of the most challenging problems in recognizing characters with hand-held cameras is that captured images undergo motion blur due to the vibration of the hand. Since it is difficult to remove the motion blur from small characters via image restoration, we propose a recognition method without de-blurring. The proposed method includes a generative learning method in the training step to simulate blurred images by controlling blur parameters. The method consists of two steps. The first step recognizes the blurred characters based on the subspace method, and the second one reclassifies structurally similar characters using blur parameters estimated from the camera motion. We have experimentally proved that the effective use of motion blur improves the recognition accuracy of camera-captured characters.
[show abstract][hide abstract] ABSTRACT: We propose a method to recognize the visibility of traffic signals from a driver's perspective. The more that driver assistance systems are equipped for practical use, the more information that is being provided for drivers. So each information provision system should select appropriate information based on the situation. Our goal is to realize a system that recognizes the visibility of traffic signals from images taken by in-vehicle cameras and appropriately provides information to drivers. In this paper, we propose a method to measure visibility by two criterions, detectability and discriminability. Each index is computed using image processing techniques. Experiments using actual images showed that the proposed indices correspond well to human perception.
[show abstract][hide abstract] ABSTRACT: We propose a method for interpolation between eigenspaces. Techniques that represent observed patterns as multivariate normal dis- tribution have actively been developed to make it robust over observation noises. In the recognition of images that vary based on continuous pa- rameters such as camera angles, one cause that degrades performance is training images that are observed discretely while the parameters are var- ied continuously. The proposed method interpolates between eigenspaces by analogy from rotation of a hyper-ellipsoid in high dimensional space. Experiments using face images captured in various illumination condi- tions demonstrate the validity and effectiveness of the proposed interpo- lation method.
Computer Vision - ACCV 2007, 8th Asian Conference on Computer Vision, Tokyo, Japan, November 18-22, 2007, Proceedings, Part II; 01/2007
[show abstract][hide abstract] ABSTRACT: We evaluated a prostate biopsy strategy for cancer detection using a computer simulation system with virtual needle biopsy for three-dimensional (3D) prostate models.
Two 3D prostate models with a volume of 25 cc or 50 cc were constructed from computed tomographic images of radical prostatectomy specimens. The peripheral zone (PZ) and transition zone (TZ) were arranged in the prostate models according to the anatomical information. Four thousand patterns of cancer lesions were automatically inserted into each prostate model with a proportion of 75% in PZ and 25% in TZ. Average hit rates (AHR) in prostate models were evaluated both with an increased number of biopsy cores and various angles of virtual needle biopsy. The influence of adding secondary tumors for hit rates was also evaluated.
For both sizes, the laterally angled biopsy in 4-8 core biopsy schemes showed higher AHR than the vertical plane biopsy, while the vertical plane biopsy in 10-18 core biopsy schemes showed higher AHR than the laterally angled biopsy. A higher number of biopsy cores increased the AHR of secondary tumors.
Our results suggest that it is important in prostate cancer detection to change the needle placement according to the number of biopsy cores and the size of the prostate.
International Journal of Urology 11/2006; 13(10):1296-303. · 1.73 Impact Factor
[show abstract][hide abstract] ABSTRACT: In this paper, we propose a method to detect raindrops from in-vehicle camera images and recognize rainfall using time-series information. We aim to improve the accuracy of raindrop detection by averaging the test images and frame-matching the result of raindrop detection in multiple adjoining frames. According to an evaluation experiment, raindrops were detected precisely enough for automatic wiper control by the proposed method
Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on; 10/2006
[show abstract][hide abstract] ABSTRACT: When objects are recognized by using multiple cameras, recognition rates strongly depend on the camera arrangement. In this paper, we propose a new method for planning a multiple camera arrangement for accurate recognition. We use a parametric eigenspace method for the recognition framework in which objects are represented as manifolds in an eigenspace. The proposed method evaluates the adequacy of camera arrangement according to the relations between the manifolds in the eigenspace. In the experiments, we defined a function that measures relations by the distances between manifolds. The experimental results show the effectiveness of the proposed method.
18th International Conference on Pattern Recognition (ICPR 2006), 20-24 August 2006, Hong Kong, China; 01/2006
[show abstract][hide abstract] ABSTRACT: We present a novel training method for recognizing traffic sign symbols undergoing image degradations. In order to cope with the degradations, it is desirable to use similarly degraded images as training data. Our method artificially generates these data from an original image in accordance with the actual degradations. We experimentally confirmed the usefulness of our method for the camera-based traffic sign recognition
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on; 01/2006
[show abstract][hide abstract] ABSTRACT: In this paper, we propose a method of detecting liver can- cers from dynamic X-ray computed tomography (CT) images based on a two-dimensional histogram analysis. In the diagnosis of a liver, a doc- tor examines dynamic CT images. These consist of four images, namely the pre-contrast phase, early phase, portal phase, and late phase ones, which are taken sequentially within a few minutes. Since the early and late phase images are important for diagnosing liver cancer, our method refers to both of them for detecting suspicious regions and eliminating false positives. First, it extracts liver cancer candidates by applying an adaptive neighbor type filter to the late phase image. Then, precise can- cerous regions are specified by a region forming method. Most of the false positive regions are eliminated by two-dimensional histogram analysis of each region of interest. We applied the proposed method to 21 dynamic CT images. The results showed that sensitivity was 100% and there were 0.33 false positives per case on average.
Computer Vision - ACCV 2006, 7th Asian Conference on Computer Vision, Hyderabad, India, January 13-16, 2006, Proceedings, Part II; 01/2006