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One of the main challenges of histological image analysis is the high dimensionality of the images. This can be addressed via summarizing techniques or feature engineering. However, such approaches can limit the performance of subsequent machine learning models, particularly when dealing with highly heterogeneous tissue samples. One possible alternative is to employ unsupervised learning to determine...
Person re-identification is a critical yet challenging task in video surveillance which intends to match people over non-overlapping cameras. Most metric learning algorithms for person re-identification use symmetric matrix to project feature vectors into the same subspace to compute the similarity while ignoring the discrepancy between views. To solve this problem, we proposed an asymmetric cross-view...
This paper presents a novel person re-identification framework based on data fusion. The pipeline of the proposed method is composed of two stages. First, a metric learning paradigm is applied on a bunch of distinct feature extractors to produce an ensemble of estimated distance measures, which are subsequently penalized according to their confidence in evidencing the correct matches from the false...
Traditional image stylization is enforced by learning the mappings with an external paired training set. But in practice, people usually encounter a specific stylish image and want to transfer its style to their own pictures without the external dataset. Thus, we propose a hierarchical stylization model with limited reference particularly for oil paintings. First, the edge patch based dictionary is...
With the rising of intelligent vehicle technologies, traffic sign recognition become an essential problem in computer vision. Focusing on the traffic sign recognition under real-world scenario, this paper aims to develop novel local feature representation to improve the traffic sign recognition performance. Especially, with the local histogram feature as a basic unit, a novel histogram intersection...
Fire detection is one of the most interesting issues for surveillance. The existing approaches for the fire detection suffer from a high false positive ratio. To solve the problems, we present a patch-based fire detection algorithm with online outlier learning. In the proposed algorithm, the candidates of fire are obtained in the form of patch, while the classical candidates have been based on pixels...
Object instance recognition approaches based on the bag-of-words model are severely affected by the loss of spatial consistency during retrieval. As a result, costly RANSAC verification is needed to ensure geometric consistency between the query and the retrieved images. A common alternative is to inject geometric information directly into the retrieval procedure, by endowing the visual words with...
In this paper we propose a novel weakly-supervised feature learning approach, learning discriminative local features from image-level labelled data for image classification. Unlike existing feature learning approaches which assume that a set of additional data in the form of matching/non-matching pairs of local patches are given for learning the features, our approach only uses the image-level labels...
Image segmentation seeks to partition the pixels in images into distinct regions to assist other image processing functions such as object recognition. Over the last few years dictionary learning methods have become very popular for image processing tasks such as denoising, and recently structured low rank dictionary learning has been shown to be capable of promising results for recognition tasks...
The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, an occlusion-robust traffic sign recognition method is proposed. To achieve occlusion-robust detection, we design a cascaded tree detector based on the MN-LBP features and a cascaded tree. For occlusion-robust traffic...
Region segmentation is an important and challenging task. The applications range from tumour detection in medical imaging, computer aided surveillance, object location, pattern separation etc. Sparsity based data modelling in recent times have produced state of the art results in many image processing tasks. In this paper, we propose a semi-interactive region segmentation in sparse framework. Proper...
In this paper, we propose a quaternion-based sparse representation model for color images and its corresponding dictionary learning algorithm. Differing from traditional sparse image models, which represent RGB channels separately or process RGB channels as a concatenated real vector, the proposed model describes the color image as a quaternion vector matrix, where each color pixel is encoded as a...
Automated classification of histopathological images is an important research problem in medical imaging. Digital histopathology exhibits two principally distinct characteristics: 1) invariably histopathological images are multi-channel (color) with key geometric information spread across the color channels instead of being captured by luminance alone, and 2) the richness of geometric structures in...
The fast development of time-of-flight (ToF) cameras in recent years enables capture of high frame-rate 3D depth maps of moving objects. However, the resolution of depth map captured by ToF is rather limited, and thus it cannot be directly used to build a high quality 3D model. In order to handle this problem, we propose a novel joint example-based depth map super-resolution method, which converts...
A greedy-based approach to learn a compact and discriminative dictionary for sparse representation is presented. We propose an objective function consisting of two components: entropy rate of a random walk on a graph and a discriminative term. Dictionary learning is achieved by finding a graph topology which maximizes the objective function. By exploiting the monotonicity and submodularity properties...
We present a new method of predicting the visually salient locations in an image. The basic idea is to use the sparse coding coefficients as features and find a way to reconstruct the sparse features into a saliency map. In the training phase, we use the images and the corresponding fixation values to train a feature-based dictionary for sparse coding as well as a fixation-based dictionary for converting...
The Kinect provides an opportunity to collect large quantities of training data for visual learning algorithms relatively effortlessly. To this end we investigate learning to automatically segment humans from cluttered images (without depth information) given a bounding box. For this algorithm, obtaining a large dataset of images with segmented humans is crucial as it enables the possible variations...
Diabetic retinopathy (DR) is a complication of diabetes, which if untreated leads to blindness. DR early diagnosis and treatment improve outcomes. Automated assessment of single lesions associated with DR has been investigated for sometime. To improve on classification, especially across different ethnic groups, we present an approach using points-of-interest and visual dictionary that contains important...
This work builds upon the fact that robots can observe humans interacting with the objects in their environment, and that humans provide numerous non-visual cues to the identity of objects. In previous work, we outlined a Multiple-Cue Object Recognition (MCOR) algorithm which attempted to use multiple features of any type to produce more robust object recognition. All results so far reported with...
Automatic image annotation is crucial for keyword-based image retrieval. There is a trend focusing on utilization of machine learning techniques, which learn statistical models from annotated images and apply them to generate annotations for unseen images. In this paper we propose MAGMA - new image auto-annotation method based on building simple multivariate Gaussian models for images. All steps of...
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