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Action recognition has been one of the most popular fields of computer vision. This paper presents a novel approach to action recognition problem using the dimension reduction method, local fisher discriminant analysis, to reduce the dimension of feature descriptors as the preprocessing step after feature extraction. We propose to use sparse matrix and randomized kd-tree to modify and accelerate the...
We consider the problem of how to simultaneously and well correct the over- and under-exposure regions in a single low dynamic range (LDR) image. Recent methods typically focus on global visual quality but cannot well-correct much potential details in extremely wrong exposure areas, and some are also time consuming. In this paper, we propose a fast and detail-enhanced correction method based on automatic...
The percentage of false alarms caused by spiders in automated surveillance can range from 20–50%. False alarms increase the workload of surveillance personnel validating the alarms and the maintenance labor cost associated with regular cleaning of webs. We propose a novel, cost effective method to detect false alarms triggered by spiders/webs in surveillance camera networks. This is accomplished by...
This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications. To this end, detection quality is identified as a key factor influencing tracking performance, where changing the detector can improve tracking by up to 18.9%. Despite only using a rudimentary combination of familiar techniques such as...
Computing matching cost by Convolutional neural networks(CNNs) work well in fetching accurate dense disparity maps. But these methods still have problems: (1) they always employ equal weights for left and right images in convolutional layers, losing relational information of patches; (2) they don't solve the balance between patches' size and processing efficiency, the larger size the more information...
Similarity measuring plays as an import role in stereo matching, whether for visual data from standard cameras or for those from novel sensors such as Dynamic Vision Sensors (DVS). Generally speaking, robust feature descriptors contribute to designing a powerful similarity measurement, as demonstrated by classic stereo matching methods. However, the kind and representative ability of feature descriptors...
Reliable and repeatable evaluation of low-level (tracking) and high-level (behavior analysis) vision tasks require annotation of ground-truth information in videos. Depending on the scenarios, ground-truth annotation may be required for individual targets and/or groups of targets. Unlike the existing tools that generally allow an explicit annotation for individual targets only, we propose a tool that...
With the recent stunning success of machine learning, artificially intelligent machine vision research falls (roughly) into two camps: the big data camp and cognitive informatics camp. Big data uses statistical methods to discover latent structures that emerge from the co-occurrences of relevant features when sampling over enormous quantities of data. The cognitive informatics methods design computer...
Assistive technology enables people to achieve independence when performing daily tasks and it enhances their overall quality of life. Visual information is the basis for most navigational tasks, so visually impaired individuals are at disadvantage due to the lack of sufficient information about their surrounding environment. With recent advances in inclusive technology it is possible to extend the...
With the development in the field of three dimensional application and the hologram, it is necessary to go beyond the conventional interaction practices with machines, especially with computers. In contrast to conventional two dimensional interaction practices, we propose a novel human-to-machine three dimensional interaction practice which enables advanced interaction capabilities: pure direct 3D...
Understanding a scene provided by very high resolution (VHR) satellite imagery has become a more and more challenging problem. In this paper, we propose a new method for scene classification based on saliency computing of patches sampling from the VHR images. Sparse principal component analysis (sPCA) is then adopted to select the corresponding informative salient patches for image scene representation...
Moving ship detection is an important issue with the development of video satellite. However, it is difficult for the registration of sea scenes imaging with motion camera. In this paper, we propose a new method to detect moving ship by combining optical flow and video attention saliency for video satellite with image registration. Video visual attention is a consequence of some saliency features...
Object tracking usually suffers from the geometrical deformations and occlusions of objects. This paper presents a new method for accurate object tracking by combining the multi-angle discriminative correlation filters and key-points under the framework of Discriminative Scale Space Tracker (DSST) tracker. Experimental results demonstrate that the proposed method can produce promising tracking results...
A novel multiscale phase congruency (MPC) based analysis method is proposed in this paper for edge saliency detection and non-salient region texture suppression. Several MPC maps are proposed to be merged. Gaussian function based center priors and threshold processing are applied for the final edge saliency map generation, which can effectively suppress the textures and the detailed edges of non-salient...
Gaze estimation is the process of determining the point of gaze in the space, or the visual axis of an eye. It plays an important role in representing human attention; therefore, it can be most appropriately used in Human Computer Interaction as a means of an advance computer input. Here, the focus is to develop a gaze estimation method for Human Computer Interaction using an ordinary webcam mounted...
We present a context-aware hybrid classification system for the problem of fine-grained product class recognition in computer vision. Recently, retail product recognition has become an interesting computer vision research topic. We focus on the classification of products on shelves in a store. This is a very challenging classification problem because many product classes are visually similar in terms...
This paper presents a visual attention based convolutional neural network (CNN) to solve the image classification problem in the real complex world scene. The presented method can simulate the process of recognizing objects and find the area of interest which is related with the task. Compared with the CNN method in image classification, the model is proficient in fine-grained classification problem...
Saliency detection is a challenging problem and one of the most active research topics in the field of computer vision. Application scenarios of saliency detection range from surveillance to retrieval, from industrial safety to sports analysis. Given the broad set of techniques used in saliency detection and the fast progress in this area, in this paper we briefly survey the corresponding literature...
Visual processing of actions is supported by a network of brain regions in occipito-temporal, parietal, and premotor cortex in the primate brain, known as the Action Observation Network (AON). What remain unclear are the representational properties of each node of this network. In this study, we investigated the representational content of brain areas in AON using fMRI, representational similarity...
This paper presents a novel framework for the visual tracking problem. This framework predicts the exact location of the object using a regression. In this work, we first select an approximate region based on object location in the previous frame and then predict the exact location of the object in the current frame by a deep convolutional network that its last layer replaced with a regression. The...
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