In this paper, we developed a traffic peccancy processing system. this system employed computervision technique and image recognition technique to monitor the traffic status and recognized the vehicles violating the ...
详细信息
ISBN:
(纸本)0780381254
In this paper, we developed a traffic peccancy processing system. this system employed computervision technique and image recognition technique to monitor the traffic status and recognized the vehicles violating the traffic light, the vehicles stopping in the place where parking is not allowed, the vehicles crossing over the center real line of the traffic lanes and reversing in the driveway.
Information retrieval research has shown significant improvement and provided techniques that retrieve documents in image or text form. However, retrieval of multi-modal documents has been given very less attention. W...
详细信息
Functional Magnetic Resonance Imaging (fMRI) has opened ways to look inside active human brain. However, fMRI signal is an indirect indicator of underlying neuronal activity and has low-temporal resolution due to acqu...
详细信息
ISBN:
(纸本)9781450347532
Functional Magnetic Resonance Imaging (fMRI) has opened ways to look inside active human brain. However, fMRI signal is an indirect indicator of underlying neuronal activity and has low-temporal resolution due to acquisition process. this paper proposes autoregressive hidden Markov model with missing data (AR-HMM-md) framework which aims at addressing aforementioned issues while allowing accurate capturing of fMRI time series characteristics. the proposed work models unobserved neuronal activity over time as sequence of discrete hidden states, and shows how exact inference can be obtained with missing fMRI data under the "Missing not at Random" (MNAR) mechanism. this mechanism requires explicit modelling of the missing data along withthe observed data. the performance is evaluated by observing convergence characteristic of log-likelihoods and classification capability of the proposed model over existing models for two fMRI datasets. the classification is performed between real fMRI time series from a task-based experiment and randomly-generated time series. Another classification experiment is performed between children and elder subjects using fMRI time series from resting-state data. the proposed model captured the fMRI characteristics efficiently and thus converged to better posterior probability resulting into higher classification accuracy over existing models for boththe datasets.
Recent past has seen an inexorable shift towards the use of deep learning techniques to solve a myriad of problems in the field of medical imaging. In this paper, a novel segmentation method involving a fully-connecte...
详细信息
ISBN:
(纸本)9781450347532
Recent past has seen an inexorable shift towards the use of deep learning techniques to solve a myriad of problems in the field of medical imaging. In this paper, a novel segmentation method involving a fully-connected deep neural network called Deep Segmentation Network (DSN) is proposed to perform supervised regression for brain extraction from T1-weighted magnetic resonance (MR) images. In contrast to the existing patch-based feature learning techniques, DSN works on full 3D volumes, simplifying pre- and post processing operations, to efficiently provide a voxel-wise binary mask delineating the brain region. the model is evaluated using three publicly available datasets and is observed to either outdo or perform comparably to the state-of-the-art methods. DSN is able to achieve a maximum and minimum Dice Similarity Coefficient (DSC) of 97.57 and 92.82 respectively across all the datasets. Experiments conducted in this paper highlight the ability of the DSN model to automatically learn feature representations;making it a simple yet highly effective approach for brain segmentation. Preliminary experiments also suggest that the proposed model has the potential to segment sub-cortical structures accurately.
We present an efficient GPU technique for rendering rich geometric detail (e.g., surface mesostructure) of complex surfaces. We use sphere-tracing aided by directional distance maps (DDMs) to quickly find ray-mesostru...
详细信息
the image segmentation plays an important role in medical imageprocessing. Many segmentation algorithms exist. Most of them produce raster data which is not suitable for further 3D geometrical modeling of tissues. In...
详细信息
ISBN:
(纸本)0889865981
the image segmentation plays an important role in medical imageprocessing. Many segmentation algorithms exist. Most of them produce raster data which is not suitable for further 3D geometrical modeling of tissues. In this paper, a vector segmentation algorithm based on an adaptive Delaunay triangulation is proposed. Triangular meshes are used to divide an image into several non-overlapping regions whose characteristics are similar. Novel methods for improving quality of the mesh and its adaptation to the image structure are also presented.
Drought stress detection involves multi-modal image analysis with high spatio-temporal resolution. Identification of digital traits that characterizes drought stress response (DSR) is challenging due to high volume of...
详细信息
Scene Graph Generation has gained much attention in computervision research withthe growing demand in image understanding projects like visual question answering, image captioning, self-driving cars, crowd behavior ...
详细信息
Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with tra...
详细信息
ISBN:
(纸本)9781450366151
Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning frameworks is particularly challenging. this paper presents a novel formulation based on Reinforcement Learning (RL) that generates fail safe trajectories wherein the SLAM generated outputs do not deviate largely from their true values. Quintessentially, the RL framework successfully learns the otherwise complex relation between perceptual inputs and motor actions and uses this knowledge to generate trajectories that do not cause failure of SLAM. We show systematically in simulations how the quality of the SLAM dramatically improves when trajectories are computed using RL. Our method scales effectively across Monocular SLAM frameworks in both simulation and in real world experiments with a mobile robot.
the transmission of block-coded images over wireless channels results in lost blocks. In this paper, we propose a new error concealment method for covering up the high packet losses of an original image after its tran...
详细信息
暂无评论