This study aims to enhance the training efficiency of guide dogs by employing computer vision to collect training data and analyze the movements of both trainers and dogs. This task is challenging, owing to the constant movement of cameras and unstable reference points for camera calibration, which stem from the complexities of the guide dog training process and the surrounding environment. In addition, trainers and dogs walk side by side, making it difficult for 2D videos to capture complete interactions without obstacles. We present a comprehensive system that starts with 2D video footage from multicamera setups, proceeds to extrinsic camera calibration from the moving trainer's joints, and reconstructs the 3D poses of guide dogs and trainers. This process includes human 2D/3D pose estimation, camera calibration, and dog 2D/3D pose estimation. A novel aspect of the proposed approach involves modifying the existing calibration method for multiple cameras. This modification is designed to achieve extrinsic camera calibration and accommodating complex camera settings in real-world situations, including both fixed and moving multicamera setups without calibration objects. We can create a 3D representation of the training sessions by detecting the trainer's 2D and 3D skeletons and using calibrated cameras to triangulate the dog's 3D pose. This allows for a detailed analysis and adjustment of guide dog training methods based on the 3D pose data of trainers and guide dogs, thereby improving the overall training process.

In recent years, the need for guide dogs has increased, and a more efficient methodology for training guide dog trainers is accordingly required. One challenge is that haptic information, which is a significant part of guide-dog training, is difficult to explain and visualize. Virtual reality (VR) systems have attracted attention owing to their ability to support high-level immersive interactions with the presence of multisensory experiences. A haptic-enabled VR system could address the limitations of the conventional method and support novice trainers in practicing in an immersive and remote transmission manner. This study presents a handle-based sensing setup for recording haptic experiences of trainers during guide dog training for use in a VR system. Considering trainers perceive and apply forces via handle to obtain and control dog motion status, the applied forces are decoded as haptic information and recorded using corresponding sensors. While accuracy of proposed setup for collecting required data is checked to be high(average errors of validating forces and yaw angles are 0.4N and 0.93° respectively), we believe that the proposed recording system is able to measure haptic experiences for further haptic experience re-establishments in proposed VR guide-dog training system and could finally facilitate the education of a large number of dog trainers.

This paper presents a classification method for a Computer-Aided Diagnosis (CAD) system in a colorectal magnified Narrow Band Imaging (NBI) endoscopy. For the classification of a histologic findings, we consider an output result of a lesion endoscopic image from a pre-learned Convolutional Neural Network (CNN) as a feature vector and construct a set of Support Vector Machines (SVMs) by learning a set of the CNN feature vectors. In the video images, each frame has appearance changes such as blur, color shift, reflection of light and so on and it affects classification results. To improve the robustness of CAD system, we construct the SVM learned by multiple image sizes data sets so as to adapt to the noise peculiar to the video image. We confirmed that the proposed method achieves higher robustness, stable, and high classification accuracy in the endoscopic video image. The proposed method also can cope with differences in resolution by old and new endoscopes and perform stably with respect to the input endoscopic video image. We evaluated the proposed method on a customizable embedded DSP core implemented into a FPGA based prototyping system.

This paper presents a hardware implementation of a computer-aided diagnosis system that can classify lesions in real time for endoscopic video image. Unlike still images, video images contain many color shifts, blurs, reflections, and so on, a classification method that takes these noises into account is indispensable. In this paper, in the framework of Bag-of-Feature (BoF), we use a pre-learned convolutional Neural Network (CNN) with general images as feature extraction and Support Vector Machine (SVM) as classification. A robust classification method for endoscopic video is proposed by relearning SVM with several sets of endoscopic images. In addition, since CNN and SVM need to perform many multiplication and accumulation (MAC) operations, we implement the hardware system on a customizable DSP, which can be realize at high speed MAC operations and parallel processing with Very Long Instruction Word (VLIW). The prototyped system can be achieved real-time processing on video frame rate (30 fps @ 200 MHz). The proposed system enables not only to classify the presence or absence of cancer in the lesion but also to identify the progress of cancer and shows the clinical doctor as an objective indicator of the lesion in real time during the endoscopy examination.

In this paper, the computer-aided diagnosis system for colorectal endoscopic images is proposed. The proposed system is consisted of Convolutional Neural Network (CNN) as the feature extraction processing and Support Vector Machine (SVM) as identification processing. The proposed system is also implemented on customizable Digital Signal Processing (DSP) core: Vision P6 DSP and is demonstrated the effectiveness of a real-time recognition system by a FPGA based prototyping system, Protium™ S1.

BACKGROUND AND OBJECTIVE: A computer-aided system for colorectal endoscopy could provide endoscopists with important helpful diagnostic support during examinations. A straightforward means of providing an objective diagnosis in real time might be for using classifiers to identify individual parts of every endoscopic video frame, but the results could be highly unstable due to out-of-focus frames. To address this problem, we propose a defocus-aware Dirichlet particle filter (D-DPF) that combines a particle filter with a Dirichlet distribution and defocus information. METHODS: We develop a particle filter with a Dirichlet distribution that represents the state transition and likelihood of each video frame. We also incorporate additional defocus information by using isolated pixel ratios to sample from a Rayleigh distribution. RESULTS: We tested the performance of the proposed method using synthetic and real endoscopic videos with a frame-wise classifier trained on 1671 images of colorectal endoscopy. Two synthetic videos comprising 600 frames were used for comparisons with a Kalman filter and D-DPF without defocus information, and D-DPF was shown to be more robust against the instability of frame-wise classification results. Computation time was approximately 88ms/frame, which is sufficient for real-time applications. We applied our method to 33 endoscopic videos and showed that the proposed method can effectively smoothen highly unstable probability curves under actual defocus of the endoscopic videos. CONCLUSION: The proposed D-DPF is a useful tool for smoothing unstable results of frame-wise classification of endoscopic videos to support real-time diagnosis during endoscopic examinations.

In this work, various wavelet based methods like the discrete wavelet transform, the dual-tree complex wavelet transform, the Gabor wavelet transform, curvelets, contourlets and shearlets are applied for the automated classification of colonic polyps. The methods are tested on 8 HD-endoscopic image databases, where each database is acquired using different imaging modalities (Pentax's i-Scan technology combined with or without staining the mucosa), 2 NBI high-magnification databases and one database with chromoscopy high-magnification images. To evaluate the suitability of the wavelet based methods with respect to the classification of colonic polyps, the classification performances of 3 wavelet transforms and the more recent curvelets, contourlets and shearlets are compared using a common framework. Wavelet transforms were already often and successfully applied to the classification of colonic polyps, whereas curvelets, contourlets and shearlets have not been used for this purpose so far. We apply different feature extraction techniques to extract the information of the subbands of the wavelet based methods. Most of the in total 25 approaches were already published in different texture classification contexts. Thus, the aim is also to assess and compare their classification performance using a common framework. Three of the 25 approaches are novel. These three approaches extract Weibull features from the subbands of curvelets, contourlets and shearlets. Additionally, 5 state-of-the-art non wavelet based methods are applied to our databases so that we can compare their results with those of the wavelet based methods. It turned out that extracting Weibull distribution parameters from the subband coefficients generally leads to high classification results, especially for the dual-tree complex wavelet transform, the Gabor wavelet transform and the Shearlet transform. These three wavelet based transforms in combination with Weibull features even outperform the state-of-the-art methods on most of the databases. We will also show that the Weibull distribution is better suited to model the subband coefficient distribution than other commonly used probability distributions like the Gaussian distribution and the generalized Gaussian distribution. So this work gives a reasonable summary of wavelet based methods for colonic polyp classification and the huge amount of endoscopic polyp databases used for our experiments assures a high significance of the achieved results.

In this study, we have proposed an improvement for feature extraction in computer-aided diagnosis (CAD) system for colorectal endoscopic images with narrow-band imaging (NBI) magnification. Dense Scale-Invariant Feature Transform (D-SIFT) is used in the feature extraction. It is necessary to consider a trade-off between the precision of the feature extraction and speedup by the FPGA implementation for processing of real time full high definition image. In this paper, we reduced the number of dimensions for feature representation in hardware implementation purpose.

We propose a new 2T mask read only memory (ROM) with dynamic column source bias control technique, which enables achieving both high-speed operation and low power consumption. It is also possible to overcome the inherent problem of crosstalk between the bitlines. The fabricated 128-kb ROM macro using 28-nm high- k and metal-gate CMOS bulk technology realizes 0.72 ns read access time at the typical 0.85-V supply voltage, which is comparable to that of recent high-speed embedded static random access memories. The measured dynamic power dissipation is reduced by 50% compared to the conventional 2T ROM. The standby leakage can also be reduced to half that of conventional macros.

This paper proposes a method for smoothing the posterior probabilities obtained from classification results of time series input. We deal with this problem as a filtering problem with Dirichlet distribution and develop a particle filtering for this task. As a practical example of smoothing, we apply the proposed method to stabilizing NBI endoscopy recognition results over time. Experimental results demonstrate that our approach can effectively smooth highly unstable probability curves.

We newly propose the Non-Contact Stiffness Imager that can provide us with the pattern of stiffness distribution of environment. It is composed of a force probe and a displacement sensor capable of obtaining area information. The main application of the Stiffness Imager is diagnosis, especially for examining whether there exist tissues suffered by disease or not. In order to show the effectiveness, we implement the Stiffness Imager into an endoscope camera for obtaining inner stiffness information as well as surface information of stomach. Basic experimental results are also shown.

This paper proposes a new endoscope system with an air injection based contact probe capable of measuring the local impedance of the inner surface of stomach. By utilizing air pressure, we can produce an equivalent contact force, which is really advantageous for avoiding any damage for stomach during an examination. A big issue is that there is no way to obtain the applied force to stomach exactly. The key for solving it is the specially designed solenoid valve with the response for more than 500 Hz. The solenoid valve with such a high response allows us to estimate each impedance parameter, even with a reference value. We show experimental system with a couple of interesting results.

This paper discusses the implementation using mobile agent and the performance of the market computing to allocate network quality of service most efficiently, based on users' preference. Though the protection of users' private preference and the efficient calculation are both important, these two requirements often contradict each other. By implementing QoS Market, a market-based network resource allocation system, using mobile agents, the communication time between agents and an auctioneer, which is the largest overhead in market computing, is reduced dramatically without leeking users' privacy information. The result of experiments show that a mobile agent approach is more efficient than others, and the overhead of one market computing cycle in this system is about 450 msec, which means that the system achieves sufficient efficiency.

The authors have developed a meal-assistance robot for severely handicapped people. The authors' robot's high degree of practical performance is due to the new features like the hand mechanism that can handle many kinds of food and the man-machine interface controlled only by head motion. The robot was used by a handicapped person, and was given a good evaluation.