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Fast and accurate segmentation of musculoskeletal ultrasound images is an on-going challenge. Two principal factors make this task difficult: firstly, the presence of speckle noise arising from the interference that accompanies all coherent imaging approaches; secondly, the sometimes subtle interaction between musculoskeletal components that leads to non-uniformity of the image intensity. Our work...
We demonstrate how to map Local Binary Patterns (LBP), a class of leading feature extractors, onto a neuromorphic processor such as TrueNorth, a silicon expression of a non-von Neumann, low-power, spiking-based, brain-inspired processor. The application is presented in the form of a texture feature extractor that can process 8-bit grayscale video at 30fps. While consuming less than 140mW of power,...
In this paper, we present an efficient single image dehazing approach via scene-adaptive segmentation and improved dark channel model. First, we detect the image depth information and segment the raw image into the close view and distant view. Then, we utilize the minimum channel image of distant view to regularize the atmospheric veil and simultaneously estimate its light value of close view within...
We propose a new non-parametric level set model for automatic image clustering and segmentation based on non-negative matrix factorization (NMF). We show that NMF: (i) clusters the image into distinct homogeneous regions and (ii) provides the local spatial distribution of each region within the image. Furthermore, NMF has a controllable resolution and can discover homogeneous regions as small as one...
This paper presents a method for airport detection from optical satellite images using deep convolutional neural networks (CNN). To achieve fast detection with high accuracy, region proposal by searching adjacent parallel line segments has been applied to select candidate fields with potential runways. These proposals were further classified by a CNN model transfer learned from AlexNet to identify...
Convolutional neural networks have shown great promise in both general image segmentation problems as well as bioimage segmentation. In this paper, the application of different convolutional network architectures is explored on the C. elegans live/dead assay dataset from the Broad Bioimage Benchmark Collection. These architectures include a standard convolutional network which produces single pixel...
In the recognition of osteosarcoma magnetic resonance images (MRI), the probability of a pixel belonging to a class is not only related to its own features, but also closely correlated with the information distribution of the surrounding pixels. However, it is currently unable to recognize the osteosarcoma lesions and surrounding issues simultaneously. In order to solve the problem, we propose a fully...
Due to the diversity of body movements and uncertainty of recording occasion, human action recognition is still a challenging task, especially in real world. This paper provides a new method of representing the video with mid-level vision representation which is extracted from the discriminative supervoxels. In the proposed method, the discriminative supervoxels we extracted through a learning phase...
This paper proposes a novel approach that combines specialized pairwise classifiers trained with different feature subsets for facial expression classification. The proposed approach first detects and extracts automatically faces from images. Next, the face is split into several regular zones and textural features are extracted from each zone to capture local information. The features extracted from...
Echocardiographic exams allow the observation and extraction of measures related to cardiac structures. In the longitudinal parasternal view, these measures include the left ventricle end-diastolic and end-systolic diameters, end-diastolic interventricular septum thickness (IVSd), and end-diastolic left ventricle posterior wall thickness (LVPWd). Among these measures, the IVSd is important for diagnosing...
This paper proposes an intrinsically distributed cellular automata (CA) based approach to address the perennial problem of real time segmentation and classification of high dimensional images, such as remote sensing hyperspectral images. This approach is efficiently implemented on GPUs providing results that improve on the state of the art algorithms presented in the literature. It is based on the...
Capsule endoscopy (CE), introduced as a modality for non-invasive examination of entire gastrointestinal tract, demands for an efficient computer-aided decision making system to relieve the physician from the responsibility of screening around 60,000 video frames per patient. An automatic and robust segmentation algorithm can aid the automation of CE screening and decision making procedure. In this...
In most sparse coding based subspace clustering problems, using the non-convex lp-norm minimization (0 < p < 1) can often deliver better results than using the convex l1-norm minimization. In this paper, we propose a sparse subspace clustering via joint lp-norm and l2,p-norm minimization, where the lp-norm imposed on sparse representations can achieve more sparsity for clustering while l2,p-norm...
This paper proposes a novel superpixel-based method for the classification of hyperspectral image. A superpixel segmentation algorithm called entropy rate superpixel is applied to extract the spatial contextual information in the hyperspectral image, which can change the size and shape of the superpixel adaptively according to spatial structures. Then, a joint sparse representation model is applied...
Semantic context is an important and useful cue for scene parsing in complicated natural images with a substantial amount of variations in objects and the environment. This paper proposes Spatially Constrained Location Prior (SCLP) for effective modelling of global and local semantic context in the scene in terms of inter-class spatial relationships. Unlike existing studies focusing on either relative...
In this paper, we propose a novel approach for detecting multiple changes from two multi-temporal images. Despite the development of the change vector analysis (CVA) framework and its improved version the compressed CVA (C2VA) framework, it is found that they are limited when tackling the multi-change detection task for the images with one channel. Also, the intensity itself is fragile due to the...
As a graph-based clustering approach, dominant sets clustering determines the number of clusters automatically and possesses some other nice properties. By applying histogram equalization transformation to the similarity matrix before clustering, we are able to accomplish the dominant sets clustering process without any user-specified parameters. However, this transformation usually leads to over-segmented...
Pixel clustering is one of the basic methods for image segmentation. A critical problem of pixel clustering is how to measure the similarity between colors of the pixels on human visual perception. In this paper, we propose an adaptive clustering method for image segmentation, namely Prototypes Extraction and Merging (PEM) method. We first build a prototype network based on the Hebbian learning rule...
Image segmentation refers to the process of dividing an image into multiple regions which represent meaningful areas. Image segmentation is an essential step for most image analysis tasks such as object recognition and tracking, pattern recognition, content-based image retrieval, etc. In recent years, a large number of image segmentation algorithms have been developed, but achieving accurate segmentation...
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