21 research outputs found
Assistive Systems for the Visually Impaired Based on Image Processing
In this chapter, we proposed three assistive systems for visually impaired individuals based on image processing: Kinect cane system, Kinect goggle system, and light checking system. The Kinect cane system can detect obstacles of various sizes and also recognize objects such as seats. A visually impaired user is notified of the results of detection and recognition by means of vibration feedback. The Kinect goggle system is another type of wearable system, and can make user’s hands free. The light checking system is implemented as an application for a smartphone, and can tell a visually impaired user the ON/OFF states of room lights and elevator button lights. The experimental results demonstrate that the proposed systems are effective in helping visually impaired individuals in everyday environments
Smartphone-Based Escalator Recognition for the Visually Impaired
It is difficult for visually impaired individuals to recognize escalators in everyday environments. If the individuals ride on escalators in the wrong direction, they will stumble on the steps. This paper proposes a novel method to assist visually impaired individuals in finding available escalators by the use of smartphone cameras. Escalators are recognized by analyzing optical flows in video frames captured by the cameras, and auditory feedback is provided to the individuals. The proposed method was implemented on an Android smartphone and applied to actual escalator scenes. The experimental results demonstrate that the proposed method is promising for helping visually impaired individuals use escalators
A Spot Reminder System for the Visually Impaired Based on a Smartphone Camera
The present paper proposes a smartphone-camera-based system to assist visually impaired users in recalling their memories related to important locations, called spots, that they visited. The memories are recorded as voice memos, which can be played back when the users return to the spots. Spot-to-spot correspondence is determined by image matching based on the scale invariant feature transform. The main contribution of the proposed system is to allow visually impaired users to associate arbitrary voice memos with arbitrary spots. The users do not need any special devices or systems except smartphones and do not need to remember the spots where the voice memos were recorded. In addition, the proposed system can identify spots in environments that are inaccessible to the global positioning system. The proposed system has been evaluated by two experiments: image matching tests and a user study. The experimental results suggested the effectiveness of the system to help visually impaired individuals, including blind individuals, recall information about regularly-visited spots
Planning of observation and motion for interpretation of road intersection scenes considering uncertainty
Interactive Segmentation of Pancreases in Abdominal Computed Tomography Images and Its Evaluation Based on Segmentation Accuracy and Interaction Costs
The present paper proposed an interactive segmentation method of pancreases in abdominal computed tomography (CT) images based on the anatomical knowledge of medical doctors and the statistical information of pancreas shapes. This segmentation method consisted of two phases: training and testing. In the training phase, pancreas regions were manually extracted from sample CT images for training, and then a probabilistic atlas (PA) was constructed from the extracted regions. In the testing phase, a medical doctor selected seed voxels for a pancreas and background in a CT image for testing by use of our graphical user interface system. The homography transformation was used to fit the PA to the seeds. The graph cut technique whose data term was weighted by the transformed PA was applied to the test image. The seed selection, the atlas transformation, and the graph cut were executed iteratively. This doctor-in-the-loop segmentation method was applied to actual abdominal CT images of fifteen cases. The experimental results demonstrated that the proposed method was more accurate and effective than the conventional graph cut
Pulmonary Nodule Detection from X-ray CT Images Based on Region Shape Analysis and Appearance-based Clustering
In this paper, we propose a detection method of pulmonary nodules in X-ray computed tomography (CT) scans by use of three image filters and appearance-based k-means clustering. First, voxel values are suppressed in radial directions so as to eliminate extra regions in the volumes of interest (VOIs). Globular regions are enhanced by moment-of-inertia tensors where the voxel values in the VOIs are regarded as mass. Excessively enhanced voxels are reduced based on displacement between the VOI centers and the gravity points of the voxel values in the VOIs. Initial nodule candidates are determined by these filtering processings. False positives are reduced by, first, normalizing the directions of intensity distributions in the VOIs by rotating the VOIs based on the eigenvectors of the moment-of-inertia tensors, and then applying an appearance-based two-step k-means clustering technique to the rotated VOIs. The proposed method is applied to actual CT scans and experimental results are shown
Article Recognition of Pulmonary Nodules in Thoracic CT Scans Using 3-D Deformable Object Models of Different Classes
algorithm